ENH: update ISS to use Segmentation pipeline component, revert to usi…

…ng cloudfront
spacetx · Aug 15, 2018 · 13d4636 · 13d4636
1 parent af9db68
commit 13d4636
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Showing 5 changed files with 29 additions and 33 deletions.
diff --git a/notebooks/ISS_Pipeline_-_Breast_-_1_FOV.ipynb b/notebooks/ISS_Pipeline_-_Breast_-_1_FOV.ipynb
@@ -201,7 +201,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from starfish.image._registration import Registration\n",
+    "from starfish.image import Registration\n",
     "\n",
     "registration = Registration.FourierShiftRegistration(\n",
     "    upsampling=1000, \n",
@@ -386,22 +386,22 @@
    "outputs": [],
    "source": [
     "from starfish.constants import Indices\n",
-    "from starfish.image._segmentation.watershed import _WatershedSegmenter\n",
+    "from starfish.image import Segmentation\n",
     "\n",
     "dapi_thresh = .16  # binary mask for cell (nuclear) locations\n",
     "stain_thresh = .22  # binary mask for overall cells // binarization of stain\n",
-    "size_lim = (10, 10000)\n",
-    "disk_size_markers = None\n",
-    "disk_size_mask = None\n",
     "min_dist = 57\n",
     "\n",
     "stain = np.mean(primary_image.max_proj(Indices.CH, Indices.Z), axis=0)\n",
     "stain = stain/stain.max()\n",
     "nuclei_projection = nuclei.max_proj(Indices.ROUND, Indices.CH, Indices.Z)\n",
     "\n",
-    "\n",
-    "seg = _WatershedSegmenter(nuclei_projection, stain)  # uses skimage watershed.\n",
-    "cells_labels = seg.segment(dapi_thresh, stain_thresh, size_lim, disk_size_markers, disk_size_mask, min_dist)\n",
+    "seg = Segmentation.Watershed(\n",
+    "    dapi_threshold=dapi_thresh,\n",
+    "    input_threshold=stain_thresh,\n",
+    "    min_distance=min_dist\n",
+    ")\n",
+    "seg.run(primary_image, nuclei)\n",
     "seg.show()"
    ]
   },

diff --git a/notebooks/MERFISH_Pipeline_-_U2O2_Cell_Culture_-_1_FOV.ipynb b/notebooks/MERFISH_Pipeline_-_U2O2_Cell_Culture_-_1_FOV.ipynb
@@ -19,15 +19,6 @@
     "%autoreload 2"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import slicedimage"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -55,7 +46,7 @@
    "source": [
     "# load the data from cloudfront\n",
     "experiment = Experiment()\n",
-    "experiment.read('http://czi.starfish.data.public.s3.amazonaws.com/20180802/MERFISH/fov_001/experiment_new.json')"
+    "experiment.read('https://dmf0bdeheu4zf.cloudfront.net/20180802/MERFISH/fov_001/experiment.json')"
    ]
   },
   {

diff --git a/notebooks/py/ISS_Pipeline_-_Breast_-_1_FOV.py b/notebooks/py/ISS_Pipeline_-_Breast_-_1_FOV.py
@@ -121,7 +121,7 @@
 # EPY: END markdown
 
 # EPY: START code
-from starfish.image._registration import Registration
+from starfish.image import Registration
 
 registration = Registration.FourierShiftRegistration(
     upsampling=1000, 
@@ -235,22 +235,22 @@
 
 # EPY: START code
 from starfish.constants import Indices
-from starfish.image._segmentation.watershed import _WatershedSegmenter
+from starfish.image import Segmentation
 
 dapi_thresh = .16  # binary mask for cell (nuclear) locations
 stain_thresh = .22  # binary mask for overall cells // binarization of stain
-size_lim = (10, 10000)
-disk_size_markers = None
-disk_size_mask = None
 min_dist = 57
 
 stain = np.mean(primary_image.max_proj(Indices.CH, Indices.Z), axis=0)
 stain = stain/stain.max()
 nuclei_projection = nuclei.max_proj(Indices.ROUND, Indices.CH, Indices.Z)
 
-
-seg = _WatershedSegmenter(nuclei_projection, stain)  # uses skimage watershed.
-cells_labels = seg.segment(dapi_thresh, stain_thresh, size_lim, disk_size_markers, disk_size_mask, min_dist)
+seg = Segmentation.Watershed(
+    dapi_threshold=dapi_thresh,
+    input_threshold=stain_thresh,
+    min_distance=min_dist
+)
+seg.run(primary_image, nuclei)
 seg.show()
 # EPY: END code
 

diff --git a/notebooks/py/MERFISH_Pipeline_-_U2O2_Cell_Culture_-_1_FOV.py b/notebooks/py/MERFISH_Pipeline_-_U2O2_Cell_Culture_-_1_FOV.py
@@ -14,10 +14,6 @@
 # EPY: ESCAPE %autoreload 2
 # EPY: END code
 
-# EPY: START code
-import slicedimage
-# EPY: END code
-
 # EPY: START code
 # EPY: ESCAPE %matplotlib notebook
 
@@ -35,7 +31,7 @@
 # EPY: START code
 # load the data from cloudfront
 experiment = Experiment()
-experiment.read('http://czi.starfish.data.public.s3.amazonaws.com/20180802/MERFISH/fov_001/experiment_new.json')
+experiment.read('https://dmf0bdeheu4zf.cloudfront.net/20180802/MERFISH/fov_001/experiment.json')
 # EPY: END code
 
 # EPY: START markdown

diff --git a/starfish/image/_segmentation/watershed.py b/starfish/image/_segmentation/watershed.py
@@ -1,3 +1,5 @@
+from typing import Tuple, Optional
+
 import numpy as np
 import scipy.ndimage.measurements as spm
 import regional
@@ -22,6 +24,7 @@ def __init__(self, dapi_threshold, input_threshold, min_distance, **kwargs) -> N
         self.dapi_threshold = dapi_threshold
         self.input_threshold = input_threshold
         self.min_distance = min_distance
+        self._segmentation_instance: Optional[_WatershedSegmenter] = None
 
     @classmethod
     def add_arguments(cls, group_parser) -> None:
@@ -44,8 +47,8 @@ def run(self, hybridization_stack: ImageStack, nuclei_stack: ImageStack) -> regi
         disk_size_mask = None
 
         nuclei = nuclei_stack.max_proj(Indices.ROUND, Indices.CH, Indices.Z)
-        seg = _WatershedSegmenter(nuclei, stain)
-        cells_labels = seg.segment(
+        self._segmentation_instance = _WatershedSegmenter(nuclei, stain)
+        cells_labels = self._segmentation_instance.segment(
             self.dapi_threshold, self.input_threshold, size_lim, disk_size_markers, disk_size_mask,
             self.min_distance
         )
@@ -54,6 +57,12 @@ def run(self, hybridization_stack: ImageStack, nuclei_stack: ImageStack) -> regi
 
         return regions
 
+    def show(self, figsize: Tuple[int, int]=(10, 10)) -> None:
+        if isinstance(self._segmentation_instance, _WatershedSegmenter):
+            self._segmentation_instance.show(figsize=figsize)
+        else:
+            raise RuntimeError('Run segmentation before attempting to show results.')
+
 
 # TODO dganguli: fill in these types & document
 class _WatershedSegmenter: