From 1ec552299de9a5e36cefa516504269ed29fd481d Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Fri, 21 Feb 2020 14:42:33 -0800 Subject: [PATCH 01/13] added watershed tutorial and fixed typo in MinDistanceLabel docstring --- .../index.rst | 62 +++++ examples/plot_watershed_segmentation.py | 248 ++++++++++++++++++ .../morphology/Filter/min_distance_label.py | 2 +- 3 files changed, 311 insertions(+), 1 deletion(-) create mode 100644 examples/plot_watershed_segmentation.py diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index 8f765024e..ca462f1a8 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -142,6 +142,68 @@ Finding and Decoding Spots Segmenting Cells ---------------- +Unlike single-cell RNA sequencing, image-based transcriptomics methods do not physically separate +cells before acquiring RNA information. Therefore in order to characterize cells, the RNA must be +assigned into single cells by partitioning the image volume. Accurate unsupervised cell-segmentation +is an open problem for all biomedical imaging disciplines ranging from digital pathology to +neuroscience. + +The challenge of segmenting cells depends on the structural complexity of the sample and quality +of images available. For example a sparse cell mono-layer with a strong cytosol stain would be +trivial to segment but a dense heterogeneous population of cells in 3D tissue with only a DAPI stain +can be impossible to segment perfectly. On the experimental side, selecting good cell stains and +acquiring images with low background will make segmenting a more tractable task. + +There are many approaches for segmenting cells from image-based transcriptomics assays. If you do +not know which method to use, a safe bet is to start with classic thresholding and watershed. On +the other hand, if you can afford to manually segment... + +Thresholding and Watershed +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The traditional method for segmenting cells in fluorescence microscopy images is to threshold the +image into foreground pixels and background pixels and then label connected foreground as +individual cells. Common issues that affect thresholding such as background noise can be corrected +by preprocessing images before thresholding and filtering connected components after. There are +`many automated image thresholding algorithms `_ but currently +starfish requires manually selecting a global threshold value in :py:class:`.ThresholdBinarize`. + +When overlapping cells are labeled as one connected component, they are typically segmented by +using a `distance transformation followed by the watershed algorithm `_. Watershed is a classic image +processing algorithm for separating objects in images and can be applied to all types of images. +Pairing it with a distance transform is particularly useful for segmenting convex shapes like +cells. + +A segmentation pipeline that consists of thresholding, connected component analysis, and watershed +is the simplest and fastest to implement but its accuracy is highly dependent on image quality. +The signal-to-noise ratio of the cell stain must be high enough for minimal errors after +thresholding and binary operations. And the nuclei or cell shapes must be convex to meet the +assumptions of the watershed algorithm or else it will over-segment. Starfish includes the basic +functions to build a watershed segmentation pipeline and a predefined :py:class:`.Watershed` +segmentation class that uses the :term:`primary images` as the cell stain: + +:ref:`Ways to segment by thresholding and watershed in starfish` + +Machine Learning Methods +^^^^^^^^^^^^^^^^^^^^^^^^ + +Ilastik + +Manually Defining Cells +^^^^^^^^^^^^^^^^^^^^^^^ + +The most accurate but time-consuming approach is to manually segment using a tool like ROI +manager in FIJI ImageJ. +Available methods for segmenting cells. + +Points to make: +accuracy is determined by comparison to ground truth, which is just manual assessment. +the process of image segmentation: +https://imagej.net/plugins/index.html#segmentation + +:ref:`Watershed Segmentation` .. _section_assigning_spots: Assigning Spots to Cells diff --git a/examples/plot_watershed_segmentation.py b/examples/plot_watershed_segmentation.py new file mode 100644 index 000000000..1238c9f5d --- /dev/null +++ b/examples/plot_watershed_segmentation.py @@ -0,0 +1,248 @@ +""" +.. _tutorial_watershed_segmentation: + +Watershed Segmentation +====================== + +In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA +spots must be assigned to cells. One approach is to binarize images of stained cellular structures +to define cell boundaries. A problem arises when there are contiguous or partially overlapping +cells, as is often the case in tissue. These clumped cells are mislabeled as one cell by connected +component labeling. + +Watershed segmentation can be used to divide connected objects like clumped cells by finding +watershed lines that separate pixel intensity basins. The classic method for computing pixel +intensity values from a binary image is applying a distance transform, which labels foreground +pixels furthest from the background with the lowest values and pixels close to the background +with higher values. The overall effect after watershed is to segment objects into convex shapes, +which is generally a good approximation for a cell or nucleus. More details about the watershed +algorithm can be found +`here `_. + +Starfish allows users to implement watershed segmentation into their pipelines in two ways. The +first is to use :py:class:`.WatershedSegment` to explicitly define a +segmentation pipeline. The second is to use :py:class:`.Watershed`, a predefined watershed +segmentation pipeline that uses watershed segmented nuclei as seeds to run +watershed segmentation on cell stain images. + +The predefined watershed segmentation pipeline will not work for all data, so this tutorial +will first show you how you can replicate the predefined watershed segmentation pipeline using the +classes and methods provided in :py:mod:`.morphology`. Then this tutorial will cover how to run +the predefined segmentation pipeline. + +Inputs for this tutorial are :py:class:`.ImageStack`\s: +* registered primary images to mimic a cell stain +* registered nuclei images to seed the water segmentation of cells + +Output is a :py:class:`.BinaryMaskCollection`: +* each binary mask in the collection is a labeled cell +""" + +# First we need to create our inputs by filtering and registering in situ sequencing (ISS) data. + +import os +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from showit import image +from starfish.image import ApplyTransform, LearnTransform, Filter +from starfish.types import Axes, Levels +from starfish import data, FieldOfView + +matplotlib.rcParams["figure.dpi"] = 150 + +use_test_data = os.getenv("USE_TEST_DATA") is not None +experiment = data.ISS(use_test_data=use_test_data) +fov = experiment.fov() + +imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES) # primary images +dots = fov.get_image("dots") # reference round for image registration +nuclei = fov.get_image("nuclei") # nuclei` + +# filter raw data +masking_radius = 15 +filt = Filter.WhiteTophat(masking_radius, is_volume=False) +filt.run(imgs, in_place=True) +filt.run(dots, in_place=True) +filt.run(nuclei, in_place=True) + +# register primary images to reference round +learn_translation = LearnTransform.Translation(reference_stack=dots, axes=Axes.ROUND, upsampling=1000) +transforms_list = learn_translation.run(imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max")) +warp = ApplyTransform.Warp() +warp.run(imgs, transforms_list=transforms_list, in_place=True) + +################################################################################################### +# Example of Custom Watershed Pipeline +# ==================================== +# +# Starfish allows the user to build a custom watershed segmentation pipeline. This tutorial +# will demonstrate how construct a pipeline by replicating the algorithm behind starfish's +# :py:class:`.Watershed` segmentation. +# +# In order to use this algorithm, the :term:`primary images` must have higher +# background intensity in intracellular regions than extracellular regions such that the +# intracellular regions can be labeled foreground by thresholding. This is not always the case +# since it is usually beneficial to tune the microscope parameters to minimize background and +# increase the SNR of spots in the acquired primary images. :term:`` with cell +# stains would be ideal for this purpose and should be used instead if experimentally feasible. +# +# :term:`` with nuclear stains (e.g. DAPI) are also required in this algorithm +# to seed watershed segmentation of the cells. While a distance transformation of the binarized +# cell stain images could also be used to seed watershed, nuclear stained images are almost +# always included in an experiment. The advantage of using nuclei is that it will result in a +# more accurate number of cells since it is not as prone to over-segmentation artifacts as using +# the distance transform and nuclear stains are usually more robust than cellular stains. +# +# There are a number of parameters that need to be tuned for optimal segmentation. Generally, +# the same parameters can be used across an experiment unless there is variation in microscope +# settings or tissue characteristics (e.g. autofluorescence). + +# import methods for transformations on or to morphological data +from starfish.morphology import Binarize, Filter, Merge, Segment + +# set parameters +dapi_thresh = .18 # global threshold value for nuclei images +stain_thresh = .22 # global threshold value for primary images +min_dist = 57 # minimum distance (pixels) between nuclei distance transformed peaks +min_allowed_size = 10 # minimum size (in pixels) of nuclei +max_allowed_size = 10000 # maximum size (in pixels) of nuclei + +################################################################################################### +# This segmentation is done on 2D images so :py:class:`.reduce` is used to maximum intensity project +# and scale the primary and nuclei image volumes. Displaying the projected and scaled images can +# be useful for choosing thresholds. + +mp = imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max") +stain = mp.reduce( + {Axes.ROUND}, + func="mean", + level_method=Levels.SCALE_BY_IMAGE) + +nuclei_mp_scaled = nuclei.reduce( + {Axes.ROUND, Axes.CH, Axes.ZPLANE}, + func="max", + level_method=Levels.SCALE_BY_IMAGE) + +f = plt.figure(figsize=(12,5)) +ax1 = f.add_subplot(121) +nuclei_numpy = nuclei_mp_scaled._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE) +image(nuclei_numpy, ax=ax1, size=20, bar=True) +plt.title('Nuclei') + +ax2 = f.add_subplot(122) +image( + stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE), + ax=ax2, size=20, bar=True) +plt.title('Stain') + +#################################################################################################### +# In order to obtain the seeds to watershed segment cells, the nuclei are labeled first. +# :py:class:`.ThresholdBinarize` thresholds and :py:class:`.MinDistanceLabel` labels the +# binarized nuclei by using a distance transform followed by watershed. :py:class:`.AreaFilter` +# then filters nuclei by area. The :py:param:`min_dist` parameter that limits how close two +# peaks in the distance transformed image can be is key to preventing over-segmentation of nuclei +# with non-circular morphology but may also incorrectly merge two nuclei in close proximity. +# +# The binarized and segmented nuclei can be viewed to determine whether parameters need to be +# adjusted. + +binarized_nuclei = Binarize.ThresholdBinarize(dapi_thresh).run(nuclei_mp_scaled) +labeled_masks = Filter.MinDistanceLabel(min_dist, 1).run(binarized_nuclei) +watershed_markers = Filter.AreaFilter(min_area=min_allowed_size, max_area=max_allowed_size).run(labeled_masks) + +plt.subplot(121) +image( + binarized_nuclei.uncropped_mask(0).squeeze(Axes.ZPLANE.value).values, + bar=False, + ax=plt.gca(), +) +plt.title('Nuclei Thresholded') + +plt.subplot(122) +image( + watershed_markers.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values, + size=20, + cmap=plt.cm.nipy_spectral, + ax=plt.gca(), +) +plt.title('Found: {} cells'.format(len(watershed_markers))) + +#################################################################################################### +# The cell stain image (ie. primary image) is binarized and then the union of the binary cell +# image and binary nuclei image is used to create a mask for watershed. This ensures that the +# nuclei markers that seed watershed segmentation of cells are all within cells and that no area +# outside of cells is labeled. + +thresholded_stain = Binarize.ThresholdBinarize(stain_thresh).run(stain) +markers_and_stain = Merge.SimpleMerge().run([thresholded_stain, watershed_markers]) +watershed_mask = Filter.Reduce( + "logical_or", + lambda shape: np.zeros(shape=shape, dtype=np.bool) +).run(markers_and_stain) + +image( + watershed_mask.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values, + bar=False, + ax=plt.gca(), +) +plt.title('Watershed Mask') + +#################################################################################################### +# The final step is to run seeded watershed segmentation. The pixel values of the cell stain +# define the topology of the watershed basins. The basins are filled starting from seeds, +# which are the nuclei markers. And the boundaries of the basins are the watershed mask. + +segmenter = Segment.WatershedSegment(connectivity=np.ones((1, 3, 3), dtype=np.bool)) +masks = segmenter.run( + stain, + watershed_markers, + watershed_mask, +) + +# display result +image( + masks.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values, + size=20, + cmap=plt.cm.nipy_spectral, + ax=plt.gca(), +) +plt.title('Segmented Cells') + +################################################################################################### +# Pre-defined Watershed Segmentation Pipeline +# =========================================== +# +# Running :py:class:`.Watershed` from the :py:mod:`.image` module (not to be confused with +# :py:class:`.WatershedSegment` from the :py:mod:`.morphology` module) is a convenient method to +# apply the same segmentation algorithm that was built in the previous section of this tutorial. +# It sets the :py:param:`min_allowed_size` and :py:param:`max_allowed_size` of the nuclei to 10 +# pixels and 1,000 pixels, respectively, but accepts +# +# Here is an example of how to run :py:class:`.Watershed` on the same set of images as the +# previous section. The intermediate results are saved as attributes of the +# :py:class:`.Watershed` instance and can be displayed to assess performance. + +from starfish.image import Segment + +# set parameters +dapi_thresh = .18 # global threshold value for nuclei images +stain_thresh = .22 # global threshold value for primary images +min_dist = 57 # minimum distance (pixels) between nuclei distance transformed peaks + +seg = Segment.Watershed( + nuclei_threshold=dapi_thresh, + input_threshold=stain_thresh, + min_distance=min_dist +) +masks = seg.run(imgs, nuclei) +image( + masks.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values, + size=20, + cmap=plt.cm.nipy_spectral, + ax=plt.gca(), +) +plt.title('Segmented Cells') + +# display intermediate images and result +seg.show() diff --git a/starfish/core/morphology/Filter/min_distance_label.py b/starfish/core/morphology/Filter/min_distance_label.py index 1afc0caaf..1320301d9 100644 --- a/starfish/core/morphology/Filter/min_distance_label.py +++ b/starfish/core/morphology/Filter/min_distance_label.py @@ -21,7 +21,7 @@ class MinDistanceLabel(FilterAlgorithm): minimum_distance_xy : int The minimum distance between the peaks along the x or y axis. (default: 1) minimum_distance_z : int - The minimum distance between the peaks along the x or y axis. (default: 1) + The minimum distance between the peaks along the z axis. (default: 1) exclude_border : bool Exclude the borders for consideration for peaks. (default: False) """ From 28d5371f4ed4f02abf3fddf914c150e5bd6e0ff7 Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Fri, 21 Feb 2020 16:44:45 -0800 Subject: [PATCH 02/13] had to remove ':py:param:' because it's not recognized by sphinx --- examples/plot_watershed_segmentation.py | 4 ++-- expression | Bin 1160 -> 1360 bytes 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_watershed_segmentation.py b/examples/plot_watershed_segmentation.py index 1238c9f5d..c7eec83cd 100644 --- a/examples/plot_watershed_segmentation.py +++ b/examples/plot_watershed_segmentation.py @@ -140,7 +140,7 @@ # In order to obtain the seeds to watershed segment cells, the nuclei are labeled first. # :py:class:`.ThresholdBinarize` thresholds and :py:class:`.MinDistanceLabel` labels the # binarized nuclei by using a distance transform followed by watershed. :py:class:`.AreaFilter` -# then filters nuclei by area. The :py:param:`min_dist` parameter that limits how close two +# then filters nuclei by area. The ``min_dist`` parameter that limits how close two # peaks in the distance transformed image can be is key to preventing over-segmentation of nuclei # with non-circular morphology but may also incorrectly merge two nuclei in close proximity. # @@ -216,7 +216,7 @@ # Running :py:class:`.Watershed` from the :py:mod:`.image` module (not to be confused with # :py:class:`.WatershedSegment` from the :py:mod:`.morphology` module) is a convenient method to # apply the same segmentation algorithm that was built in the previous section of this tutorial. -# It sets the :py:param:`min_allowed_size` and :py:param:`max_allowed_size` of the nuclei to 10 +# It sets the ``min_allowed_size`` and ``max_allowed_size`` of the nuclei to 10 # pixels and 1,000 pixels, respectively, but accepts # # Here is an example of how to run :py:class:`.Watershed` on the same set of images as the diff --git a/expression b/expression index 5a0e9f0b74b9f481070b6a6751910d2d82edbed8..baec30c9d7df3ab669cc6da9ca9cebd8263edf2b 100644 GIT binary patch delta 487 zcmeC+yudZVpOIr?fU~Yna%xUad}fM5UTJPpYEgWCT6}3CbuvtOzvUgm^_C`14?tC^XnZ9u5PUM>M6Cq;mBJwZO0x5 z!-CseN?u)d(5v+iZ~GqO;QriqAT4!XCs%ZerA9o&8&3*vbC!@+#>f-_DB=69n0K<3wtqu{@Hsygg+Z`<8r{!d_E^u&RSRN^~Vvz&GE8 D9lVXB delta 378 zcmcb>)xkNzpOJlHfU~Gjr9xs+YNA4wLWM$dYEDjkW(ott!~^<_B@;imGm1>MV033p zncTqW#>Bj3@(Cb^WpV+N786Uz#3tLY2uzM)k(m64NnrAQ77h%4 z4hv^}c*iAkGbI)Wd#{y$cs%Djgm0@u@2L5C$2iBq z|7Y)U{#A|+${XzETD;ddxEvL~H@khMgU`YlIVKKSP7V$h2Dfe{q&S4`Zrzf$f476| z@!&7luFP~0`)*WNBR`7)4LCsg_8%Y&2ZqW2nDi#Muy{=FVc{?V3NisPD-g2-F$WNH a12GQ}^8qnG5DNgYu)C|DYrG?vbOHb?zIE~d From 3639d4338c7c6e192a11ecf98a443569936505da Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Fri, 21 Feb 2020 16:47:56 -0800 Subject: [PATCH 03/13] fixed broken references --- docs/source/creating_an_image_processing_pipeline/index.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index ca462f1a8..34f5eab23 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -201,9 +201,8 @@ Available methods for segmenting cells. Points to make: accuracy is determined by comparison to ground truth, which is just manual assessment. the process of image segmentation: -https://imagej.net/plugins/index.html#segmentation +`segmentation in imagej `_ -:ref:`Watershed Segmentation` .. _section_assigning_spots: Assigning Spots to Cells From 132c56ff5f3f1f72ffc9bad8e83704aea0ebb34a Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Fri, 21 Feb 2020 16:58:35 -0800 Subject: [PATCH 04/13] fix ':term:' not in glossary --- examples/plot_watershed_segmentation.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/examples/plot_watershed_segmentation.py b/examples/plot_watershed_segmentation.py index c7eec83cd..259e94d91 100644 --- a/examples/plot_watershed_segmentation.py +++ b/examples/plot_watershed_segmentation.py @@ -84,15 +84,17 @@ # background intensity in intracellular regions than extracellular regions such that the # intracellular regions can be labeled foreground by thresholding. This is not always the case # since it is usually beneficial to tune the microscope parameters to minimize background and -# increase the SNR of spots in the acquired primary images. :term:`` with cell -# stains would be ideal for this purpose and should be used instead if experimentally feasible. +# increase the SNR of spots in the acquired primary images. :term:`Auxiliary images` with cell stains would be ideal for this purpose and should be used instead if +# experimentally feasible. # -# :term:`` with nuclear stains (e.g. DAPI) are also required in this algorithm -# to seed watershed segmentation of the cells. While a distance transformation of the binarized -# cell stain images could also be used to seed watershed, nuclear stained images are almost -# always included in an experiment. The advantage of using nuclei is that it will result in a -# more accurate number of cells since it is not as prone to over-segmentation artifacts as using -# the distance transform and nuclear stains are usually more robust than cellular stains. +# :term:`Auxiliary images ` with nuclear stains (e.g. DAPI) are also required in +# this algorithm to seed watershed segmentation of the cells. While a distance transformation of +# the binarized cell stain images could also be used to seed watershed, nuclear stained images +# are almost always included in an experiment. The advantage of using nuclei is that it will +# result in a more accurate number of cells since it is not as prone to over-segmentation +# artifacts as using the distance transform and nuclear stains are usually more robust than +# cellular stains. # # There are a number of parameters that need to be tuned for optimal segmentation. Generally, # the same parameters can be used across an experiment unless there is variation in microscope From 19adc96305e451979c0bae416848d0fc72858940 Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Mon, 24 Feb 2020 14:48:24 -0800 Subject: [PATCH 05/13] completed manual segmentation tutorial --- .../index.rst | 64 ++++++++++++------ examples/RoiSet.zip | Bin 0 -> 5349 bytes examples/plot_ilastik_segmentation.py | 60 ++++++++++++++++ examples/plot_manual_segmentation.py | 59 ++++++++++++++++ examples/plot_watershed_segmentation.py | 4 +- 5 files changed, 163 insertions(+), 24 deletions(-) create mode 100644 examples/RoiSet.zip create mode 100644 examples/plot_ilastik_segmentation.py create mode 100644 examples/plot_manual_segmentation.py diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index 34f5eab23..2e34d36d7 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -145,8 +145,8 @@ Segmenting Cells Unlike single-cell RNA sequencing, image-based transcriptomics methods do not physically separate cells before acquiring RNA information. Therefore in order to characterize cells, the RNA must be assigned into single cells by partitioning the image volume. Accurate unsupervised cell-segmentation -is an open problem for all biomedical imaging disciplines ranging from digital pathology to -neuroscience. +is an `open problem `_ for all biomedical imaging +disciplines ranging from digital pathology to neuroscience. The challenge of segmenting cells depends on the structural complexity of the sample and quality of images available. For example a sparse cell mono-layer with a strong cytosol stain would be @@ -154,9 +154,15 @@ trivial to segment but a dense heterogeneous population of cells in 3D tissue wi can be impossible to segment perfectly. On the experimental side, selecting good cell stains and acquiring images with low background will make segmenting a more tractable task. -There are many approaches for segmenting cells from image-based transcriptomics assays. If you do -not know which method to use, a safe bet is to start with classic thresholding and watershed. On -the other hand, if you can afford to manually segment... +There are many approaches for segmenting cells from image-based transcriptomics assays. Below are +a few methods that are implemented or integrated with starfish to output a :py:class:` +.BinaryMaskCollection`, which represents a collection of labeled objects. If you do not know which +segmentation method to use, a safe bet is to start with thresholding and watershed. On the other +hand, if you can afford to manually define ROI masks there is no better way to guarantee accurate +segmentation. + +.. note:: + The "ground truth" for cell segmentation is manually segmented references. Thresholding and Watershed ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -171,37 +177,53 @@ starfish requires manually selecting a global threshold value in :py:class:`.Thr When overlapping cells are labeled as one connected component, they are typically segmented by using a `distance transformation followed by the watershed algorithm `_. Watershed is a classic image -processing algorithm for separating objects in images and can be applied to all types of images. -Pairing it with a distance transform is particularly useful for segmenting convex shapes like -cells. +.html>`_. Watershed is a classic image processing algorithm for separating objects in images and +can be applied to all types of images. Pairing it with a distance transform is particularly +useful for segmenting convex shapes like cells. A segmentation pipeline that consists of thresholding, connected component analysis, and watershed is the simplest and fastest to implement but its accuracy is highly dependent on image quality. The signal-to-noise ratio of the cell stain must be high enough for minimal errors after thresholding and binary operations. And the nuclei or cell shapes must be convex to meet the -assumptions of the watershed algorithm or else it will over-segment. Starfish includes the basic +assumptions of the distance transform or else it will over-segment. Starfish includes the basic functions to build a watershed segmentation pipeline and a predefined :py:class:`.Watershed` segmentation class that uses the :term:`primary images` as the cell stain: :ref:`Ways to segment by thresholding and watershed in starfish` -Machine Learning Methods +Manually Defining Cells +^^^^^^^^^^^^^^^^^^^^^^^ + +The most accurate but time-consuming approach is to manually segment images using a tool such as +`ROI manager `_ in FIJI (ImageJ). It is a +straightforward process that starfish supports by allowing ROI set binaries to be imported as a +:py:class:`.BinaryMaskCollection`. These masks can then be integrated into the pipeline for +visualization and assigning spots to cells. + +:ref:`Loading ImageJ ROI set` + +Machine-Learning Methods ^^^^^^^^^^^^^^^^^^^^^^^^ -Ilastik +Besides the two classic cell segmentation approaches mentioned above, there are machine-learning +methods that aim to replicate the accuracy of manual cell segmentation while reducing the labor +required. Machine-learning algorithms for segmentation are continually improving but there is no +perfect solution for all image types yet. These methods require training data (e.g. stained +images with manually defined labels) to train a model to predict cell or nuclei locations in test +data. There are `exceptions that don't require training on your specific data `_ but generally training the model is something to consider when evaluating how much time +each segmentation approach will require. -Manually Defining Cells -^^^^^^^^^^^^^^^^^^^^^^^ +Starfish currently has built-in functionality to support `ilastik `_, a +segmentation toolkit that leverages machine-learning. Ilastik has a Pixel Classification +workflow that performs semantic segmentation of the image, returning probability maps for each +label such as cells and background. To transform the images of pixel probabilities to binary +masks, you can use the same thresholding and watershed methods in starfish that are used for +segmenting images of stained cells. + +:ref:`Working with ilastik` -The most accurate but time-consuming approach is to manually segment using a tool like ROI -manager in FIJI ImageJ. -Available methods for segmenting cells. -Points to make: -accuracy is determined by comparison to ground truth, which is just manual assessment. -the process of image segmentation: -`segmentation in imagej `_ .. _section_assigning_spots: diff --git a/examples/RoiSet.zip b/examples/RoiSet.zip new file mode 100644 index 0000000000000000000000000000000000000000..5bf468473eecb6342d005cac73d5298ce64bbb7f GIT binary patch literal 5349 zcmb7{dvH@#9>;GUy-Au=XiA|cZGlL6Y_)-NlAGHUummeXEM==O*a}EeXrXI)`2cie zCn0$4<3jO;mDLp}gYN2#r7+!&uPWJOtbqp z_k_dYSU9r2(VwH6p*t9`)USYNals0sKV92?4t51Pn=k33se3w&iywS8(9*m*2ZT@U zoSij48^K^D3wD76OnIrnIQLYy`B8FCC;5~oqr~m9BM0xGl}LNKTcSVJ2j2=-gpILD z;-laJ_%l2lsIv%Tgz@4$(Fs@K>%I%xOq0JidZY2Qh_~a$R~s%_qd?%;Gog| z%bG(PnC>pU~N5FM|Nh!LZ#vJ}1lS`ATnDoK@`i`2`BF{Sc@F7$H#%Qwg9vgCR> z;b(XvQ_Hg!AzK(FP7tSxYdxRkmT02)c{N^x*DQ<#P4J_(ZEP6VDgh zB5y>DJr1@JYGr93ufD)9A+lmgN^@|efIL@3Neg%VB;2$f%GQmPrI}%`=SAxqFc*%& zOMQo{GX0&A_1l8Yv&FsQV~raum-8f-r~xP8{qWzgrET{iP5qtvjx!8MvpyHxZwZ+~ z`7PD#%F2F)hHJ-itBAUuBo)fdR>W~Cg?&wz^rhK};`Ihr!l=_bRdP$Y-WlGptcERS zrwJ}Fmx$(2FM0$c|CzsOmI|vr6VDsd=g>Qb8=^sYrTn@J#xp}(nZoFJO^3aIev)Hw= zz^iDfub%m$3KWBCPzLV8>v%f;yJpTrI2k)oHo~|P9YYdx=8O-oNQtz z?b{_x{Eb!(ywR!|qUuQ{K^Jj{y;PbFjWgd0LnrqsFI8+qM zmrK)ngPcmNo=NU3<2le)mA;h6MHuyR}Qb@>AG!3_8i%nTG*1ifGegr;a{|GTtjh#)AP8_dC8p?DVV0=%Y-I8bmpLg*TnA^suYITg!o;njyWhlm1LO*g zM;*R4ebp#g)0Fzn+#;eU*-PYHY2)oU>$S@4v#3Y zMyGfUQmU||qOV7;5gMqGA62(8?Ev-!P7V0JP{72eb&`V6d()V)zbi9VX<9U(jo=78xHeRkQUaQ`KCFb_Fx=2+n>q>&Zv zhK3Wv@rA;8_Hj9BMKzK;xMk!&a@hs4y}!>K%pmi3R1|y15fM+rm9Pa)fW`Pp)PW_` zj^~H}Oy6KpC?%A?vznQ>-8x^E^={ucA3i}kD@;B~L^A9yEF@EhqsL;0@Evn^)|Cf9 z9f)bYwjDm7_O-QQt~ddtdG5=XY~Jz0a==%7c`{z0vciD^`HXi@pmL^9d7&n zZC%{Qef>UO7a>sr)L6CMqFk7stBc)bL`bqvjwjI>qavPqaz8hIm-Q7mTML%Th56j) zj{H-<|39z>oka)n3IFaGjM*gHk@Bv#9eacRj1p^TT=^eUntmPa>6v?Y4eMKdbdNb! zv!1l}q>xqC^;EaJB>v=*jDVPT6I~onKe@@_$B#^n{rE~^4tv3ISOnif-{3R&y8laG zi$3$g@OqJi=Ij!4FJ~4q1(RwF^Sa$8|J%&DU9zlABNqtqK1x=WyU=0Bg{rZ)Q9RLb zZ_M4?p}RA+!WEgVxls#i5FB2M*Fth~zGp*G!HZWIJpMEf1AFfaO-)~dTAXClxZ+uz zDyvyvyXC}!VO$=^x$U^fK@Z_u&C3Gz`g>Wr9N91PNUD1jK7~K^AzkWkkbr*CHJ-&U zMfUxkjeF!LgC3dwWT0{vARZ|z;fqJ_imyD$_cS6(+;mTOH2$?=OU~VCEHC%;T-2$V zXCXF}A*9+K5H6+Xjf|$SX$9Y}ir3^oPNF8vIoqyDJJ;xCB@?IQ%pQ)CA{SBO!n6Th zkL~XMwaMNT+%C^q3n=qE9+<5=`hsTV2K)y4!PhILZ<})u*skQAe@=(Em8VjwO4)_B zoR}kJV76Yk+HHI!Q4x(clNnk_l?!5gA*Fx*e?2WPrFrG*M)Qs2Uk&hzrBcl+_bZww zEKR`bf- zNb~Ov@rwMa=08^CJeqeJ2G3V!O*OB~V>BNf;uYCY%`0OQ%`Y(yp0CVsYF-&~XudDO zE2Ea0SEdn~7fgfaD}#}mS0)IWADcSJD-(>GR}Oue|I-k!NF8ckIh<*pHxHh#3<_#q zIYnvy^C4a_(AB(h?$LZT7(8D&dDXmfTG9ON1h1TxYF;^GXnspt;(Tss!l`-XY@m6= ah(TUCm&!|1$h%?~EBW=1Z_VVd80LQgTpr~B literal 0 HcmV?d00001 diff --git a/examples/plot_ilastik_segmentation.py b/examples/plot_ilastik_segmentation.py new file mode 100644 index 000000000..94eae10c2 --- /dev/null +++ b/examples/plot_ilastik_segmentation.py @@ -0,0 +1,60 @@ +""" +.. _tutorial_ilastik_segmentation: + +Using ilastik +====================== + +Starfish currently has built-in functionality to support `ilastik `_, +a segmentation toolkit that leverages machine-learning . Ilastik has a Pixel Classification +workflow that performs semantic segmentation of the image, returning probability maps for each +label (e.g. cells). + +In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA +spots must be assigned to cells. One approach is to binarize images of stained cellular structures +to define cell boundaries. A problem arises when there are contiguous or partially overlapping +cells, as is often the case in tissue. These clumped cells are mislabeled as one cell by connected +component labeling. + +Watershed segmentation can be used to divide connected objects like clumped cells by finding +watershed lines that separate pixel intensity basins. The classic method for computing pixel +intensity values from a binary image is applying a distance transform, which labels foreground +pixels furthest from the background with the lowest values and pixels close to the background +with higher values. The overall effect after watershed is to segment objects into convex shapes, +which is generally a good approximation for a cell or nucleus. More details about the watershed +algorithm can be found +`here `_. + +Starfish allows users to implement watershed segmentation into their pipelines in two ways. The +first is to use :py:class:`.WatershedSegment` to explicitly define a +segmentation pipeline. The second is to use :py:class:`.Watershed`, a predefined watershed +segmentation pipeline that uses watershed segmented nuclei as seeds to run +watershed segmentation on cell stain images. + +The predefined watershed segmentation pipeline will not work for all data, so this tutorial +will first show you how you can replicate the predefined watershed segmentation pipeline using the +classes and methods provided in :py:mod:`.morphology`. Then this tutorial will cover how to run +the predefined segmentation pipeline. + +Inputs for this tutorial are :py:class:`.ImageStack`\s: +* registered primary images to mimic a cell stain +* registered nuclei images to seed the water segmentation of cells + +Output is a :py:class:`.BinaryMaskCollection`: +* each binary mask in the collection is a labeled cell +""" + +################################################################################################### +# Importing ilastik Probability Map +# ==================================== +# +# Starfish allows the user to build a custom watershed segmentation pipeline. This tutorial +# will demonstrate how construct a pipeline by replicating the algorithm behind starfish's +# :py:class:`.Watershed` segmentation. + +################################################################################################### +# Calling out to ilastik Pre-trained Classifier +# ============================================= +# +# Starfish allows the user to build a custom watershed segmentation pipeline. This tutorial +# will demonstrate how construct a pipeline by replicating the algorithm behind starfish's +# :py:class:`.Watershed` segmentation. \ No newline at end of file diff --git a/examples/plot_manual_segmentation.py b/examples/plot_manual_segmentation.py new file mode 100644 index 000000000..8a8f9ed1f --- /dev/null +++ b/examples/plot_manual_segmentation.py @@ -0,0 +1,59 @@ +""" +.. _tutorial_manual_segmentation: + +Loading ImageJ ROI Sets +======================= + +.. note:: + Starfish only supports importing 2D ROIs. + +The best quality cell segmentation annotations are manually drawn by experts. If you have ROI +sets exported with ROI manager in ImageJ or FIJI they can be loaded into starfish as a +:py:class:`.BinaryMaskCollection`. The ROI set for each field of view must be passed with the +corresponding :py:class:`.ImageStack` to :py:meth:`.from_fiji_roi_set` in order to assign +accurate ``pixel ticks`` and ``physical ticks`` to the :py:class:`.BinaryMaskCollection`. + +This tutorial demonstrates how to load an ROI set that was exported form ROI manager as a .zip +and how to plot the resulting :py:class:`.BinaryMaskCollection`. +""" + +import matplotlib +import matplotlib.pyplot as plt +import os + +import starfish.data +from starfish import BinaryMaskCollection +from starfish.image import Filter +from starfish.types import Levels + +matplotlib.rcParams["figure.dpi"] = 150 + +# Load MERFISH data to get dapi ImageStack +experiment = starfish.data.MERFISH() +fov = experiment["fov_000"] +dapi = fov.get_image("nuclei") # nuclei + + +# Preprocess MERFISH dapi imagestack +def preprocess(dapi): + blur = Filter.GaussianLowPass(sigma=5) + blurred = blur.run(dapi) + + clip = Filter.Clip(p_min=1, p_max=95, level_method=Levels.SCALE_BY_CHUNK) + clipped = clip.run(blurred) + return clipped + + +dapi = preprocess(dapi) + +# Import RoiSet.zip as BinaryMaskCollection +roi_path = os.path.join(os.path.dirname("__file__"), 'RoiSet.zip') +masks = BinaryMaskCollection.from_fiji_roi_set(path_to_roi_set_zip=roi_path, original_image=dapi) + +# display BinaryMaskCollection next to original dapi image +f, (ax1, ax2) = plt.subplots(ncols=2) +ax1.imshow(dapi.xarray.values.squeeze()) +ax1.set_title("Dapi") +ax2.imshow(masks.to_label_image().xarray.values.squeeze(), cmap=plt.cm.nipy_spectral) +ax2.set_title("Imported ROI") +f.tight_layout() diff --git a/examples/plot_watershed_segmentation.py b/examples/plot_watershed_segmentation.py index 259e94d91..ea95b87c0 100644 --- a/examples/plot_watershed_segmentation.py +++ b/examples/plot_watershed_segmentation.py @@ -40,7 +40,6 @@ # First we need to create our inputs by filtering and registering in situ sequencing (ISS) data. -import os import matplotlib import matplotlib.pyplot as plt import numpy as np @@ -51,8 +50,7 @@ matplotlib.rcParams["figure.dpi"] = 150 -use_test_data = os.getenv("USE_TEST_DATA") is not None -experiment = data.ISS(use_test_data=use_test_data) +experiment = data.ISS() fov = experiment.fov() imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES) # primary images From b9a326f4b8e5231e8fdc7600652adf6be01ac7a9 Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Tue, 25 Feb 2020 21:11:51 -0800 Subject: [PATCH 06/13] completed segmentation tutorials and edited ilastik_pre_trained_probability.py to accept variable data_set names. --- .../index.rst | 47 ++--- examples/plot_ilastik_segmentation.py | 177 +++++++++++++----- examples/plot_manual_segmentation.py | 23 ++- examples/plot_watershed_segmentation.py | 15 +- .../Filter/ilastik_pre_trained_probability.py | 25 ++- .../morphology/binary_mask/binary_mask.py | 2 +- 6 files changed, 198 insertions(+), 91 deletions(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index 2e34d36d7..e97276130 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -31,8 +31,8 @@ and running it on an experiment. Loading Data ------------ -* :ref:`Formatting your data ` -* :ref:`Using formatted example data ` +* Tutorial: :ref:`Formatting your data ` +* Tutorial: :ref:`Using formatted example data ` .. _section_manipulating_images: @@ -43,8 +43,8 @@ Sometimes it can be useful subset the images by, for example, excluding out-of-f cropping out edge effects. For sparse data, it can be useful to project the z-volume into a single image, as this produces a much faster processing routine. -* :ref:`Cropping ` -* :ref:`Projecting ` +* Tutorial: :ref:`Cropping ` +* Tutorial: :ref:`Projecting ` .. _section_correcting_images: @@ -56,11 +56,11 @@ handling or microfluidices that are involved in capturing the images. These step *independent* of the assay. *Starfish* enables the user to design a pipeline that matches their imaging system -* :ref:`Illumination Correction ` -* :ref:`Chromatic Aberration ` -* :ref:`Deconvolution ` -* :ref:`Image Registration ` -* :ref:`Image Correction Pipeline ` +* Tutorial: :ref:`Illumination Correction ` +* Tutorial: :ref:`Chromatic Aberration ` +* Tutorial: :ref:`Deconvolution ` +* Tutorial: :ref:`Image Registration ` +* Tutorial: :ref:`Image Correction Pipeline ` .. _section_improving_snr: @@ -72,7 +72,7 @@ some level of autofluorescence which causes cellular compartments to have more b intracellular regions. This can confound spot finders, which look for local intensity differences. These approaches ameliorate these problems. -* :ref:`Removing Autofluorescence ` +* Tutorial: :ref:`Removing Autofluorescence ` .. _section_normalizing_intensities: @@ -116,7 +116,8 @@ How to normalize How to normalize depends on your data and a key assumption. There are two approaches for normalizing images in starfish: -:ref:`Normalizing Intensity Distributions` +Normalizing Intensity Distributions +""""""""""""""""""""""""""""""""""" If you know a priori that image volumes acquired for every channel and/or every round should have the same distribution of intensities then the intensity *distributions* of image volumes can be @@ -124,7 +125,10 @@ normalized with :py:class:`.MatchHistograms`. Typically this means the number of background autofluorescence in every image volume is approximately uniform across channels and/or rounds. -:ref:`Normalizing Intensity Values ` +* Tutorial: :ref:`Normalizing Intensity Distributions` + +Normalizing Intensity Values +"""""""""""""""""""""""""""" In most data sets the differences in gene expression leads to too much variation in number of spots between channels and rounds. Normalizing intensity distributions would incorrectly skew the @@ -132,6 +136,8 @@ intensities. Instead you can use :py:class:`.Clip`, :py:class:`.ClipPercentileTo :py:class:`.ClipValueToZero` to normalize intensity *values* by clipping extreme values and rescaling. +* Tutorial: :ref:`Normalizing Intensity Values ` + .. _section_finding_and_decoding: Finding and Decoding Spots @@ -162,7 +168,8 @@ hand, if you can afford to manually define ROI masks there is no better way to g segmentation. .. note:: - The "ground truth" for cell segmentation is manually segmented references. + While there is no "ground truth" for cell segmentation the closest approximation is manual +segmentation by an expert in the tissue of interest. Thresholding and Watershed ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -187,20 +194,20 @@ The signal-to-noise ratio of the cell stain must be high enough for minimal erro thresholding and binary operations. And the nuclei or cell shapes must be convex to meet the assumptions of the distance transform or else it will over-segment. Starfish includes the basic functions to build a watershed segmentation pipeline and a predefined :py:class:`.Watershed` -segmentation class that uses the :term:`primary images` as the cell stain: +segmentation class that uses the :term:`primary images` as the cell stain. -:ref:`Ways to segment by thresholding and watershed in starfish` +* Tutorial: :ref:`Ways to segment by thresholding and watershed` Manually Defining Cells ^^^^^^^^^^^^^^^^^^^^^^^ The most accurate but time-consuming approach is to manually segment images using a tool such as -`ROI manager `_ in FIJI (ImageJ). It is a -straightforward process that starfish supports by allowing ROI set binaries to be imported as a +`ROI manager `_ in FIJI (ImageJ). It +is a straightforward process that starfish supports by allowing ROI set binaries to be imported as a :py:class:`.BinaryMaskCollection`. These masks can then be integrated into the pipeline for visualization and assigning spots to cells. -:ref:`Loading ImageJ ROI set` +* Tutorial: :ref:`Loading ImageJ ROI set` Machine-Learning Methods ^^^^^^^^^^^^^^^^^^^^^^^^ @@ -221,9 +228,7 @@ label such as cells and background. To transform the images of pixel probabiliti masks, you can use the same thresholding and watershed methods in starfish that are used for segmenting images of stained cells. -:ref:`Working with ilastik` - - +* Tutorial: :ref:`Using ilastik in starfish` .. _section_assigning_spots: diff --git a/examples/plot_ilastik_segmentation.py b/examples/plot_ilastik_segmentation.py index 94eae10c2..26b93df20 100644 --- a/examples/plot_ilastik_segmentation.py +++ b/examples/plot_ilastik_segmentation.py @@ -1,60 +1,145 @@ """ .. _tutorial_ilastik_segmentation: -Using ilastik -====================== +Using ilastik in starfish +========================= + +In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA +spots must be assigned to cells. Segmenting a microscopy image into single cells is either +time-consuming when done manually or inaccurate when done with thresholding and watershed. The +advent of deep learning models for image segmentation promises a solution that is both fast and +accurate. Starfish currently has built-in functionality to support `ilastik `_, -a segmentation toolkit that leverages machine-learning . Ilastik has a Pixel Classification +a segmentation toolkit that leverages machine-learning. Ilastik has a Pixel Classification workflow that performs semantic segmentation of the image, returning probability maps for each -label (e.g. cells). +label (e.g. cells). To use ilastik in starfish first install ilastik locally and follow the +Pixel Classification workflow using the GUI to train a classifier, which is saved in an ilastik +project file. -In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA -spots must be assigned to cells. One approach is to binarize images of stained cellular structures -to define cell boundaries. A problem arises when there are contiguous or partially overlapping -cells, as is often the case in tissue. These clumped cells are mislabeled as one cell by connected -component labeling. - -Watershed segmentation can be used to divide connected objects like clumped cells by finding -watershed lines that separate pixel intensity basins. The classic method for computing pixel -intensity values from a binary image is applying a distance transform, which labels foreground -pixels furthest from the background with the lowest values and pixels close to the background -with higher values. The overall effect after watershed is to segment objects into convex shapes, -which is generally a good approximation for a cell or nucleus. More details about the watershed -algorithm can be found -`here `_. - -Starfish allows users to implement watershed segmentation into their pipelines in two ways. The -first is to use :py:class:`.WatershedSegment` to explicitly define a -segmentation pipeline. The second is to use :py:class:`.Watershed`, a predefined watershed -segmentation pipeline that uses watershed segmented nuclei as seeds to run -watershed segmentation on cell stain images. - -The predefined watershed segmentation pipeline will not work for all data, so this tutorial -will first show you how you can replicate the predefined watershed segmentation pipeline using the -classes and methods provided in :py:mod:`.morphology`. Then this tutorial will cover how to run -the predefined segmentation pipeline. - -Inputs for this tutorial are :py:class:`.ImageStack`\s: -* registered primary images to mimic a cell stain -* registered nuclei images to seed the water segmentation of cells - -Output is a :py:class:`.BinaryMaskCollection`: -* each binary mask in the collection is a labeled cell +There are two options for using ilastik in starfish. The first is running the trained pixel +classifier on images outside of starfish to generate a probability map that can then be loaded in +starfish as an :py:class:`.ImageStack`. The second is to run the pixel classifier from within +starfish by calling out to ilastik and passing it the trained classifier. The results will be the +same. + +The probability map is an nd-array with the same size as the input :py:class:`.ImageStack`. The +value of each element in the nd-array equals the probability of the corresponding pixel to belong to +a label. In ilastik, you can train the pixel classifier to predict the probability for multiple +labels such as nuclei, cells, and background. However, starfish will only load the probability +map of the first label so you should use the first label for cells or nuclei only. + +This tutorial will first describe how you can run a trained ilastik classifier from within a +starfish process. In the second section this tutorial will demonstrate how to load an ilastik +probability map hdf5 file as an :py:class:`.ImageStack`. The last section transforms the +:py:class:`.ImageStack` into a :py:class:`BinaryMaskCollection` that can be used by +:py:class:`.Label` to assign spots to cells. """ ################################################################################################### -# Importing ilastik Probability Map -# ==================================== +# Calling Out to ilastik Trained Pixel Classifier +# =============================================== +# +# Running a trained ilastik pixel classifier from within starfish allows a pipeline to take +# advantage of ilastik's machine-learning model without having to process images from every field +# of view, switching to ilastik to run classifier, exporting all the probability maps, and then +# loading the files back into a starfish pipeline. # -# Starfish allows the user to build a custom watershed segmentation pipeline. This tutorial -# will demonstrate how construct a pipeline by replicating the algorithm behind starfish's -# :py:class:`.Watershed` segmentation. +# The requirements for calling out to ilastik from a starfish pipeline is having ilastik +# installed locally and having an ilastik project that contains a trained classifier. The +# classifier can only be run on an :py:class:`ImageStack` with a single round and channel. +# Obviously, the :py:class:`ImageStack` should be an image of the same type that the classifier +# was trained on for expected probability results. + +# Load MERFISH data to get dapi ImageStack +import os +import matplotlib +import matplotlib.pyplot as plt + +import starfish.data +from starfish.image import Filter +from starfish.types import Axes, Coordinates, Levels + +matplotlib.rcParams["figure.dpi"] = 150 + +experiment = starfish.data.MERFISH() +fov = experiment["fov_000"] +dapi = fov.get_image("nuclei") + +# Process dapi images by blurring and clipping +def preprocess(dapi): + blur = Filter.GaussianLowPass(sigma=5) + blurred = blur.run(dapi) + clip = Filter.Clip(p_min=1, p_max=95, level_method=Levels.SCALE_BY_CHUNK) + clipped = clip.run(blurred) + return clipped + +dapi = preprocess(dapi) + +# Need ilastik script and ilastik project with trained classifier to instantiate filter +ilastik_exe_path = os.path.join(os.path.dirname("__file__"), 'run_ilastik.sh') +ilastik_proj_path = os.path.join(os.path.dirname("__file__"), 'merfish_dapi.ilp') +ipp = Filter.IlastikPretrainedProbability(ilastik_executable=ilastik_exe_path, ilastik_project=ilastik_proj_path) + +# Run IlastikPretrainedProbability filter to get probabilities of 'Label 1' as ImageStack +# probabilities = ipp.run(stack=dapi) ################################################################################################### -# Calling out to ilastik Pre-trained Classifier -# ============================================= +# Loading ilastik Probability Map +# =============================== # -# Starfish allows the user to build a custom watershed segmentation pipeline. This tutorial -# will demonstrate how construct a pipeline by replicating the algorithm behind starfish's -# :py:class:`.Watershed` segmentation. \ No newline at end of file +# If you already have probability maps of your images from ilastik or prefer to use the ilastik +# GUI it is possible to load the exported hdf5 files into starfish. When using the 'Prediction +# Export' panel in ilastik be sure to use ``Source: Probabilities`` and ``Format: hdf5``. If you +# edit the Dataset name then it must also be passed as ``dataset_name`` to +# :py:meth:`.import_ilastik_probabilities`. The example below loads the same probability map that +# would have been generated in the first section of this tutorial. + +# Need to instantiate Filter even though it is not run +ilastik_exe_path = os.path.join(os.path.dirname("__file__"), 'run_ilastik.sh') +ilastik_proj_path = os.path.join(os.path.dirname("__file__"), 'merfish_dapi.ilp') +ipp = Filter.IlastikPretrainedProbability(ilastik_executable=ilastik_exe_path, ilastik_project=ilastik_proj_path) + +# Load hdf5 file as ImageStack +h5_file_path = os.path.join(os.path.dirname("__file__"), 'dapi_Probabilities.h5') +imported_probabilities = ipp.import_ilastik_probabilities(path_to_h5_file=h5_file_path) + +# View probability map next to dapi image +f, (ax1, ax2) = plt.subplots(ncols=2) +ax1.imshow(dapi.xarray.values.squeeze()) +ax1.set_title("Dapi") +ax2.imshow(imported_probabilities.xarray.values.squeeze()) +ax2.set_title("Probabilities") +f.tight_layout() + +################################################################################################### +# Transforming Probability Map to Masks +# ===================================== +# +# In order to use ilastik semantic segmentation probabilities to assign spots to cells in +# starfish they must be transformed into binary cell masks. A straightforward approach to +# transforming the :py:class:`.ImageStack` to a :py:class:`.BinaryMaskCollection` is to threshold +# the probabilities, run connected component analysis, and watershed. This process is analogous to +# segmenting a stained cell image. + +# Scale probability map +clip = Filter.Clip(p_min=0, p_max=100, level_method=Levels.SCALE_BY_CHUNK) +clipped_probabilities = clip.run(imported_probabilities) + +# Threshold, connected components, watershed, and filter by area +from starfish.morphology import Binarize, Filter +prob_thresh = 0.05 +min_dist = 100 +min_allowed_size = 5000 +max_allowed_size = 100000 +binarized_probabilities = Binarize.ThresholdBinarize(prob_thresh).run(clipped_probabilities) +labeled_masks = Filter.MinDistanceLabel(min_dist, 1).run(binarized_probabilities) +masks = Filter.AreaFilter(min_area=min_allowed_size, max_area=max_allowed_size).run(labeled_masks) + +# Show probability map along with cell masks +f, (ax1, ax2) = plt.subplots(ncols=2) +ax1.imshow(imported_probabilities.xarray.values.squeeze()) +ax1.set_title("Probabilities") +ax2.imshow(labeled_masks.to_label_image().xarray.values.squeeze(), cmap=plt.cm.nipy_spectral) +ax2.set_title("Masks") +f.tight_layout() \ No newline at end of file diff --git a/examples/plot_manual_segmentation.py b/examples/plot_manual_segmentation.py index 8a8f9ed1f..6e0228da3 100644 --- a/examples/plot_manual_segmentation.py +++ b/examples/plot_manual_segmentation.py @@ -7,14 +7,27 @@ .. note:: Starfish only supports importing 2D ROIs. -The best quality cell segmentation annotations are manually drawn by experts. If you have ROI -sets exported with ROI manager in ImageJ or FIJI they can be loaded into starfish as a -:py:class:`.BinaryMaskCollection`. The ROI set for each field of view must be passed with the -corresponding :py:class:`.ImageStack` to :py:meth:`.from_fiji_roi_set` in order to assign -accurate ``pixel ticks`` and ``physical ticks`` to the :py:class:`.BinaryMaskCollection`. +In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA +spots must be assigned to cells by segmenting an image. The best quality cell segmentation +annotations are manually drawn by experts. If you have ROI sets exported with `ROI manager + ROI Manager +* Click "polygon selection" (third button from left on GUI) +* Create a polygon, then click the "+" button to finalize it +* Repeat until segmented +* Click "more >>>" +* Click "save" +* Save the ROISet """ import matplotlib diff --git a/examples/plot_watershed_segmentation.py b/examples/plot_watershed_segmentation.py index ea95b87c0..7acbae5f2 100644 --- a/examples/plot_watershed_segmentation.py +++ b/examples/plot_watershed_segmentation.py @@ -31,10 +31,12 @@ the predefined segmentation pipeline. Inputs for this tutorial are :py:class:`.ImageStack`\s: + * registered primary images to mimic a cell stain * registered nuclei images to seed the water segmentation of cells Output is a :py:class:`.BinaryMaskCollection`: + * each binary mask in the collection is a labeled cell """ @@ -169,7 +171,7 @@ plt.title('Found: {} cells'.format(len(watershed_markers))) #################################################################################################### -# The cell stain image (ie. primary image) is binarized and then the union of the binary cell +# The cell stain image (i.e. primary image) is binarized and then the union of the binary cell # image and binary nuclei image is used to create a mask for watershed. This ensures that the # nuclei markers that seed watershed segmentation of cells are all within cells and that no area # outside of cells is labeled. @@ -194,6 +196,8 @@ # which are the nuclei markers. And the boundaries of the basins are the watershed mask. segmenter = Segment.WatershedSegment(connectivity=np.ones((1, 3, 3), dtype=np.bool)) + +# masks is BinaryMaskCollection for downstream steps masks = segmenter.run( stain, watershed_markers, @@ -235,14 +239,9 @@ input_threshold=stain_thresh, min_distance=min_dist ) + +# masks is BinaryMaskCollection for downstream steps masks = seg.run(imgs, nuclei) -image( - masks.to_label_image().xarray.squeeze(Axes.ZPLANE.value).values, - size=20, - cmap=plt.cm.nipy_spectral, - ax=plt.gca(), -) -plt.title('Segmented Cells') # display intermediate images and result seg.show() diff --git a/starfish/core/image/Filter/ilastik_pre_trained_probability.py b/starfish/core/image/Filter/ilastik_pre_trained_probability.py index 704c4f15a..193cfa19c 100644 --- a/starfish/core/image/Filter/ilastik_pre_trained_probability.py +++ b/starfish/core/image/Filter/ilastik_pre_trained_probability.py @@ -20,8 +20,9 @@ class IlastikPretrainedProbability(FilterAlgorithm): Parameters ---------- ilastik_executable: Union[Path, str] - Path to run_ilastik.sh needed for running ilastik in headless mode. Typically the script is - located: "/Applications/{ILASTIK_VERSION}/Contents/ilastik-release/run_ilastik.sh" + Path to run_ilastik.sh (Linux and Mac) or ilastik.bat (Windows) needed for running + ilastik in headless mode. Typically the script is located: "/Applications/{ + ILASTIK_VERSION}/Contents/ilastik-release/run_ilastik.sh" ilastik_project: Union[Path, str] path to ilastik project .ilp file @@ -31,9 +32,9 @@ class IlastikPretrainedProbability(FilterAlgorithm): def __init__(self, ilastik_executable: Union[Path, str], ilastik_project: Union[Path, str]): ilastik_executable = Path(ilastik_executable) if not ilastik_executable.exists(): - raise EnvironmentError("Can not find run_ilastik.sh. Make sure you've provided the " - "correct location. If you need to download ilastik please" - " visit: https://www.ilastik.org/download.html") + raise EnvironmentError("Can not find run_ilastik.sh or ilastik.bat. Make sure you've " + "provided the correct location. If you need to download ilastik " + "please visit: https://www.ilastik.org/download.html") self.ilastik_executable = ilastik_executable self.ilastik_project = ilastik_project @@ -46,7 +47,7 @@ def run( *args, ) -> Optional[ImageStack]: """ - Use a pre trained probability pixel classification model to generate probabilites + Use a pre trained probability pixel classification model to generate probabilities for a dapi image Parameters @@ -94,8 +95,11 @@ def run( return self.import_ilastik_probabilities(output_file) @classmethod - def import_ilastik_probabilities(cls, path_to_h5_file: Union[str, Path] - ) -> ImageStack: + def import_ilastik_probabilities( + cls, + path_to_h5_file: Union[str, Path], + dataset_name: str = "exported_data" + ) -> ImageStack: """ Import cell probabilities provided by ilastik as an ImageStack. @@ -103,7 +107,8 @@ def import_ilastik_probabilities(cls, path_to_h5_file: Union[str, Path] ---------- path_to_h5_file : Union[str, Path] Path to the .h5 file outputted by ilastik - + dataset_name : str + Name of dataset in ilastik Export Image Settings Returns ------- ImageStack : @@ -111,7 +116,7 @@ def import_ilastik_probabilities(cls, path_to_h5_file: Union[str, Path] """ h5 = h5py.File(path_to_h5_file) - probability_images = h5["exported_data"][:] + probability_images = h5[dataset_name][:] h5.close() cell_probabilities, _ = probability_images[:, :, 0], probability_images[:, :, 1] label_array = ndi.label(cell_probabilities)[0] diff --git a/starfish/core/morphology/binary_mask/binary_mask.py b/starfish/core/morphology/binary_mask/binary_mask.py index a9770aca1..3800a8cac 100644 --- a/starfish/core/morphology/binary_mask/binary_mask.py +++ b/starfish/core/morphology/binary_mask/binary_mask.py @@ -279,7 +279,7 @@ def from_fiji_roi_set( path_to_roi_set_zip : Union[str, Path] Path to an external fiji roi file original_image : ImageStack - Dapi image used in fiji segmentation workflow + image from same FOV used in fiji segmentation workflow Returns -------- From 6ec8aaeb0aae5f57b8ba26d24d07ffc102534dbb Mon Sep 17 00:00:00 2001 From: Matt Cai Date: Tue, 25 Feb 2020 21:54:38 -0800 Subject: [PATCH 07/13] removed 'plot_' prefix and added necessary files --- .../index.rst | 12 ++++++------ examples/dapi_Probabilities.h5 | Bin 0 -> 36825728 bytes ...egmentation.py => ilastik_segmentation.py} | 0 ...segmentation.py => manual_segmentation.py} | 2 +- examples/merfish_dapi.ilp | Bin 0 -> 758952 bytes examples/run_ilastik.sh | 1 + ...mentation.py => watershed_segmentation.py} | 0 expression | Bin 1360 -> 1360 bytes 8 files changed, 8 insertions(+), 7 deletions(-) create mode 100644 examples/dapi_Probabilities.h5 rename examples/{plot_ilastik_segmentation.py => ilastik_segmentation.py} (100%) rename examples/{plot_manual_segmentation.py => manual_segmentation.py} (96%) create mode 100644 examples/merfish_dapi.ilp create mode 100644 examples/run_ilastik.sh rename examples/{plot_watershed_segmentation.py => watershed_segmentation.py} (100%) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index e97276130..fd7faaab1 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -161,15 +161,15 @@ can be impossible to segment perfectly. On the experimental side, selecting good acquiring images with low background will make segmenting a more tractable task. There are many approaches for segmenting cells from image-based transcriptomics assays. Below are -a few methods that are implemented or integrated with starfish to output a :py:class:` -.BinaryMaskCollection`, which represents a collection of labeled objects. If you do not know which -segmentation method to use, a safe bet is to start with thresholding and watershed. On the other -hand, if you can afford to manually define ROI masks there is no better way to guarantee accurate -segmentation. +a few methods that are implemented or integrated with starfish to output a +:py:class:`.BinaryMaskCollection`, which represents a collection of labeled objects. If you do not +know which segmentation method to use, a safe bet is to start with thresholding and watershed. On +the other hand, if you can afford to manually define ROI masks there is no better way to +guarantee accurate segmentation. .. note:: While there is no "ground truth" for cell segmentation the closest approximation is manual -segmentation by an expert in the tissue of interest. + segmentation by an expert in the tissue of interest. 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zcmcb>b%ATbHm<~?)I^0Ug-V5rjR#_wCfhJOOuo;;G1-PiU~&wQu3;9eH_F|+x#(Z7 zLqOiM2$gHT4jyxTV$Uy?ad29ZU!Bmh$sz2yfgba@;|_r{5?Q;r4IDx<`V99?k#TU$ zk6U10k>e2f&hpE=txOI9Rh!>lObvB#n0lD=!RuTHhlzzPp~<@)TszJ9p1J!v1b&e# z6n=ZcA$*7L$)}GtJA_|SI_zKLq2S>Elr6JGWu*g4Sh~%|%DoQ4jn`7DH?4HAZYaGV zz2Ny|9Tp8C2L@E&z%bd5MQm~li^t?1pg+#A1RF8~Rj>dt2M}`uF)tAF0kHrO3j(na P5DUAz`nkqCf=MR;KI34v delta 279 zcmcb>b%ATbHm(YV#G=$hg(`*0jR#_wCf{e_m~6u$Fqwr}1I{jCmaI3py0O};r_}z2 zBX7~P9eW%M3vO>Id3D)Auhu`j?R$)a`*YuoOD`8W=-$>YE0&CRaQl5Mh~w!G2lLGf z&Nv;EbMSxu;8C%}SqE1p-IW=dryL?GJcMqWZE_H}xz5Tz=AnbL&cXbp`g#rl(furp z&q5s{_B4k^Ur~3ke!rUYTWqp|L-2;Iz&|4ln35ycd&?`mXpc4z`=!Kd8E*a zMU!<{G$#8nD=;}QOs-?`o7@BR#~GGjLq-M$1|}e80b({F<^*CcAm#>QULfWJVt#j5 LKi7CiFzEyU;$UF> From 2be60975e56fda4fd2a53117f2742c3b6b497dcf Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 12:17:44 -0800 Subject: [PATCH 08/13] Update docs/source/creating_an_image_processing_pipeline/index.rst Co-Authored-By: Tony Tung --- docs/source/creating_an_image_processing_pipeline/index.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index fd7faaab1..aed2a0174 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -149,7 +149,7 @@ Segmenting Cells ---------------- Unlike single-cell RNA sequencing, image-based transcriptomics methods do not physically separate -cells before acquiring RNA information. Therefore in order to characterize cells, the RNA must be +cells before acquiring RNA information. Therefore, in order to characterize cells, the RNA must be assigned into single cells by partitioning the image volume. Accurate unsupervised cell-segmentation is an `open problem `_ for all biomedical imaging disciplines ranging from digital pathology to neuroscience. @@ -258,4 +258,3 @@ origin. At this point, it's trivial to create a cell x gene matrix. * :ref:`Spot Decoding ` * :ref:`Segmenting Cells ` * :ref:`Assigning Spots to Cells ` - From df13609d6aaf4bfc4c36446368d276c4a1d00083 Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 13:51:25 -0800 Subject: [PATCH 09/13] Update docs/source/creating_an_image_processing_pipeline/index.rst Co-Authored-By: Tony Tung --- docs/source/creating_an_image_processing_pipeline/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index aed2a0174..f4d2b6be8 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -155,7 +155,7 @@ is an `open problem `_ for all disciplines ranging from digital pathology to neuroscience. The challenge of segmenting cells depends on the structural complexity of the sample and quality -of images available. For example a sparse cell mono-layer with a strong cytosol stain would be +of images available. For example, a sparse cell mono-layer with a strong cytosol stain would be trivial to segment but a dense heterogeneous population of cells in 3D tissue with only a DAPI stain can be impossible to segment perfectly. On the experimental side, selecting good cell stains and acquiring images with low background will make segmenting a more tractable task. From b82fdf948d0c147ecd389de244856366addc31c9 Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 13:52:25 -0800 Subject: [PATCH 10/13] Update docs/source/creating_an_image_processing_pipeline/index.rst Co-Authored-By: Tony Tung --- docs/source/creating_an_image_processing_pipeline/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index f4d2b6be8..014a1a2e0 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -168,7 +168,7 @@ the other hand, if you can afford to manually define ROI masks there is no bette guarantee accurate segmentation. .. note:: - While there is no "ground truth" for cell segmentation the closest approximation is manual + While there is no "ground truth" for cell segmentation, the closest approximation is manual segmentation by an expert in the tissue of interest. Thresholding and Watershed From f695cb4b1cec62459791f9e2e7190556a33ee380 Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 13:52:50 -0800 Subject: [PATCH 11/13] Update docs/source/creating_an_image_processing_pipeline/index.rst Co-Authored-By: Tony Tung --- docs/source/creating_an_image_processing_pipeline/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/creating_an_image_processing_pipeline/index.rst b/docs/source/creating_an_image_processing_pipeline/index.rst index 014a1a2e0..954128261 100644 --- a/docs/source/creating_an_image_processing_pipeline/index.rst +++ b/docs/source/creating_an_image_processing_pipeline/index.rst @@ -189,7 +189,7 @@ can be applied to all types of images. Pairing it with a distance transform is p useful for segmenting convex shapes like cells. A segmentation pipeline that consists of thresholding, connected component analysis, and watershed -is the simplest and fastest to implement but its accuracy is highly dependent on image quality. +is simple and fast to implement but its accuracy is highly dependent on image quality. The signal-to-noise ratio of the cell stain must be high enough for minimal errors after thresholding and binary operations. And the nuclei or cell shapes must be convex to meet the assumptions of the distance transform or else it will over-segment. Starfish includes the basic From 2dad5c39f624de4ad13d34f93b011cc81d89e2ba Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 13:54:16 -0800 Subject: [PATCH 12/13] Update examples/manual_segmentation.py Co-Authored-By: Tony Tung --- examples/manual_segmentation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/manual_segmentation.py b/examples/manual_segmentation.py index 4e4417bd6..29eac961e 100644 --- a/examples/manual_segmentation.py +++ b/examples/manual_segmentation.py @@ -10,7 +10,7 @@ In order to create a cell by gene expression matrix from image-based transcriptomics data, RNA spots must be assigned to cells by segmenting an image. The best quality cell segmentation annotations are manually drawn by experts. If you have ROI sets exported with `ROI manager -`_ in ImageJ or FIJI they can be +`_ in ImageJ or FIJI, they can be loaded into starfish as a :py:class:`.BinaryMaskCollection`. The ROI set for each field of view must be passed with the corresponding :py:class:`.ImageStack` to :py:meth:`.from_fiji_roi_set` in order to assign accurate ``pixel ticks`` and ``physical ticks`` to the From f7e462232be3721f7ba3b0b97ba10b2465ed62c5 Mon Sep 17 00:00:00 2001 From: mattcai <39178165+mattcai@users.noreply.github.com> Date: Fri, 28 Feb 2020 14:24:05 -0800 Subject: [PATCH 13/13] Update ilastik_segmentation.py --- examples/ilastik_segmentation.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/ilastik_segmentation.py b/examples/ilastik_segmentation.py index 26b93df20..7dee22e9b 100644 --- a/examples/ilastik_segmentation.py +++ b/examples/ilastik_segmentation.py @@ -11,8 +11,9 @@ accurate. Starfish currently has built-in functionality to support `ilastik `_, -a segmentation toolkit that leverages machine-learning. Ilastik has a Pixel Classification -workflow that performs semantic segmentation of the image, returning probability maps for each +a segmentation toolkit that leverages machine-learning. Ilastik has a `Pixel Classification +workflow `_ +that performs semantic segmentation of the image, returning probability maps for each label (e.g. cells). To use ilastik in starfish first install ilastik locally and follow the Pixel Classification workflow using the GUI to train a classifier, which is saved in an ilastik project file. @@ -142,4 +143,4 @@ def preprocess(dapi): ax1.set_title("Probabilities") ax2.imshow(labeled_masks.to_label_image().xarray.values.squeeze(), cmap=plt.cm.nipy_spectral) ax2.set_title("Masks") -f.tight_layout() \ No newline at end of file +f.tight_layout()