diff --git a/starfish/core/morphology/binary_mask/binary_mask.py b/starfish/core/morphology/binary_mask/binary_mask.py
index 21aece936..a32f71023 100644
--- a/starfish/core/morphology/binary_mask/binary_mask.py
+++ b/starfish/core/morphology/binary_mask/binary_mask.py
@@ -1,9 +1,12 @@
 import os
+from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
 from dataclasses import dataclass
+from functools import partial
 from pathlib import Path
 from typing import (
     Any,
+    Callable,
     Hashable,
     Iterator,
     Mapping,
@@ -508,6 +511,71 @@ def to_targz(self, path: Union[str, Path]):
         with open(os.fspath(path), "wb") as fh:
             _io.BinaryMaskIO.write_versioned_binary_mask(fh, self)
 
+    def _apply(
+            self,
+            function: Callable[[np.ndarray], np.ndarray],
+            *args,
+            n_processes: Optional[int] = None,
+            **kwargs
+    ) -> "BinaryMaskCollection":
+        """Given a function that takes an ndarray and outputs another, apply that method to all the
+        masks in this collection to form a new collection.  All the masks are uncropped before being
+        passed to the function.
+
+        Parameters
+        ----------
+        function : Callable[[np.ndarray], np.ndarray]
+            A function that should produce a mask array when given a mask array.
+        n_processes : Optional[int]
+            The number of processes to use for apply. If None, uses the output of os.cpu_count()
+            (default = None).
+        """
+        if n_processes is None:
+            n_processes = os.cpu_count()
+
+        applied_func = partial(
+            BinaryMaskCollection._apply_single_mask,
+            function=function,
+            mask_collection=self,
+            args=args,
+            kwargs=kwargs,
+        )
+        selectors = range(len(self._masks))
+
+        with ThreadPoolExecutor(max_workers=n_processes) as tpe:
+            results: Iterator[MaskData] = tpe.map(applied_func, selectors)
+
+        return BinaryMaskCollection(
+            self._pixel_ticks,
+            self._physical_ticks,
+            list(results),
+            self._log,
+        )
+
+    @staticmethod
+    def _apply_single_mask(
+            mask_index: int,
+            mask_collection: "BinaryMaskCollection",
+            args: Sequence,
+            kwargs: Mapping,
+            function: Callable[[np.ndarray], np.ndarray],
+    ) -> MaskData:
+        """Given a mask collection, and an index, retrieve a mask and apply a function to that mask.
+        Return the output along with the offsets of the original mask.  If the original mask is
+        uncropped, then the offsets should all be 0.  If the original mask is not uncropped, it is
+        propagated from the input masks's offsets.
+        """
+        input_mask = mask_collection.uncropped_mask(mask_index)
+        output_mask = function(input_mask.values, *args, **kwargs)  # type: ignore
+
+        selection_range: Sequence[slice] = BinaryMaskCollection._crop_mask(output_mask)
+
+        return MaskData(
+            output_mask[selection_range],
+            tuple(selection.start for selection in selection_range),
+            None
+        )
+
 
 # these need to be at the end to avoid recursive imports
 from . import _io  # noqa
diff --git a/starfish/core/morphology/binary_mask/test/test_binary_mask.py b/starfish/core/morphology/binary_mask/test/test_binary_mask.py
index 5182e4260..a61c0986e 100644
--- a/starfish/core/morphology/binary_mask/test/test_binary_mask.py
+++ b/starfish/core/morphology/binary_mask/test/test_binary_mask.py
@@ -1,4 +1,5 @@
 import numpy as np
+from skimage.morphology import binary_dilation
 
 from starfish.core.morphology.label_image import LabelImage
 from starfish.core.types import Axes, Coordinates
@@ -157,3 +158,30 @@ def test_from_empty_label_image(tmp_path):
         original_props = binary_mask_collection.mask_regionprops(ix)
         recalculated_props = binary_mask_collection.mask_regionprops(ix)
         assert original_props == recalculated_props
+
+
+def test_apply():
+    input_mask_collection = binary_mask_collection_2d()
+    output_mask_collection = input_mask_collection._apply(binary_dilation)
+
+    assert input_mask_collection._pixel_ticks == output_mask_collection._pixel_ticks
+    assert input_mask_collection._physical_ticks == output_mask_collection._physical_ticks
+    assert input_mask_collection._log == output_mask_collection._log
+    assert len(input_mask_collection) == len(output_mask_collection)
+
+    region_0, region_1 = output_mask_collection.masks()
+
+    assert region_0.name == '0'
+    assert region_1.name == '1'
+
+    temp = np.ones((2, 6), dtype=np.bool)
+    assert np.array_equal(region_0, temp)
+    temp = np.ones((3, 4), dtype=np.bool)
+    temp[0, 0] = 0
+    assert np.array_equal(region_1, temp)
+
+    assert np.array_equal(region_0[Axes.Y.value], [0, 1])
+    assert np.array_equal(region_0[Axes.X.value], [0, 1, 2, 3, 4, 5])
+
+    assert np.array_equal(region_1[Axes.Y.value], [2, 3, 4])
+    assert np.array_equal(region_1[Axes.X.value], [2, 3, 4, 5])