def tissue_mask(wsi_input, output_path, method, resolution, units, kernel_size,
mode, file_types):
"""Generate tissue mask for a WSI."""
file_types = tuple(file_types.split(", "))
output_path = pathlib.Path(output_path)
if os.path.isdir(wsi_input):
files_all = utils.misc.grab_files_from_dir(input_path=wsi_input,
file_types=file_types)
elif os.path.isfile(wsi_input):
files_all = [
wsi_input,
]
else:
raise FileNotFoundError
if mode == "save" and not output_path.is_dir():
os.makedirs(output_path)
if method == "Otsu":
masker = tissuemask.OtsuTissueMasker()
elif method == "Morphological":
if not kernel_size:
if units == "mpp":
masker = tissuemask.MorphologicalMasker(mpp=resolution)
elif units == "power":
masker = tissuemask.MorphologicalMasker(power=resolution)
else:
raise MethodNotSupported(
"Specified units not supported for tissue masking.")
else:
masker = tissuemask.MorphologicalMasker(kernel_size=kernel_size)
else:
raise MethodNotSupported
for curr_file in files_all:
wsi = wsicore.wsireader.get_wsireader(input_img=curr_file)
wsi_thumb = wsi.slide_thumbnail(resolution=1.25, units="power")
mask = masker.fit_transform(wsi_thumb[np.newaxis, :])
if mode == "show":
im_region = Image.fromarray(mask[0])
im_region.show()
if mode == "save":
utils.misc.imwrite(
output_path.joinpath(pathlib.Path(curr_file).stem + ".png"),
mask[0].astype(np.uint8) * 255,
)
def test_morphological_min_region_size():
"""Test morphological masker with min_region_size set.
Creates a test image (0=foreground, 1=background) and applies the
morphological masker with min_region_size=6. This should output
only the largest square region as foreground in the mask
(0=background, 1=foreground).
"""
# Create a blank image of 1s
img = np.ones((10, 10))
# Create a large square region of 9 zeros
img[1:4, 1:4] = 0
# Create a row of 5 zeros
img[1, 5:10] = 0
# Create a single 0
img[8, 8] = 0
masker = tissuemask.MorphologicalMasker(kernel_size=1, min_region_size=6)
output = masker.fit_transform([img[..., np.newaxis]])
assert np.sum(output[0]) == 9
# Create the expected output with jsut the large square region
# but as ones against zeros (the mask is the inverse of the input).
expected = np.zeros((10, 10))
expected[1:4, 1:4] = 1
assert np.all(output[0] == expected)
def test_morphological_masker(_sample_svs):
"""Test simple morphological thresholding."""
wsi = wsireader.OpenSlideWSIReader(_sample_svs)
thumb = wsi.slide_thumbnail()
masker = tissuemask.MorphologicalMasker()
mask_a = masker.fit_transform([thumb])[0]
masker.fit([thumb])
mask_b = masker.transform([thumb])[0]
assert np.array_equal(mask_a, mask_b)
assert masker.threshold is not None
assert len(np.unique(mask_a)) == 2
assert mask_a.shape == thumb.shape[:2]
def test_morphological_greyscale_masker(_sample_svs):
"""Test morphological masker with greyscale inputs."""
wsi = wsireader.OpenSlideWSIReader(_sample_svs)
mpp = 32
thumb = wsi.slide_thumbnail(mpp, "mpp")
thumb = cv2.cvtColor(thumb, cv2.COLOR_RGB2GRAY)
inputs = thumb[np.newaxis, ..., np.newaxis]
masker = tissuemask.MorphologicalMasker()
mask_a = masker.fit_transform(inputs)[0]
masker.fit(inputs)
mask_b = masker.transform(inputs)[0]
assert np.array_equal(mask_a, mask_b)
assert masker.threshold is not None
assert len(np.unique(mask_a)) == 2
assert mask_a.shape == thumb.shape[:2]
def test_morphological_masker_mpp(_sample_svs):
"""Test simple morphological thresholding with mpp."""
wsi = wsireader.OpenSlideWSIReader(_sample_svs)
mpp = 32
thumb = wsi.slide_thumbnail(mpp, "mpp")
kwarg_sets = [
dict(mpp=mpp),
dict(mpp=[mpp, mpp]),
]
for kwargs in kwarg_sets:
masker = tissuemask.MorphologicalMasker(**kwargs)
mask_a = masker.fit_transform([thumb])[0]
masker.fit([thumb])
mask_b = masker.transform([thumb])[0]
assert np.array_equal(mask_a, mask_b)
assert masker.threshold is not None
assert len(np.unique(mask_a)) == 2
assert mask_a.shape == thumb.shape[:2]
def test_morphological_kernel_size_none():
"""Test giveing a None kernel size for morphological masker."""
tissuemask.MorphologicalMasker(kernel_size=None)
def test_transform_morphological_conflicting_args():
"""Test giving conflicting arguments to morphological masker."""
with pytest.raises(ValueError):
tissuemask.MorphologicalMasker(mpp=32, power=1.25)
def test_transform_before_fit_morphological():
"""Test morphological masker error on transform before fit."""
image = np.ones((1, 10, 10))
masker = tissuemask.MorphologicalMasker()
with pytest.raises(Exception):
masker.transform([image])[0]