def old_preprocessing_main(args):
if args.t is None and args.s is None and args.k is None:
raise ValueError('No preprocessing tasks were specified')
verbose_print(args, f"Preprocessing {args.input}")
if os.path.isdir(args.input):
# Load series of 2D TIFFs and process in parallel
paths, filenames = tifs_in_dir(args.input)
img = io.imread(paths[0])
shape = (len(paths), *img.shape)
if args.float:
dtype = 'float32'
else:
dtype = img.dtype
arr = io.new_zarr(args.zarr, shape=shape, dtype=dtype, chunks=tuple(args.c))
args_list = []
for i, (path, _) in enumerate(zip(paths, filenames)):
args_list.append((args, path, arr, i))
with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
list(tqdm.tqdm(pool.imap_unordered(_preprocess_image2d, args_list), total=len(args_list)))
if args.p is not None:
before = io.imread(paths[args.p])
after = arr[args.p]
elif os.path.isdir(args.input):
# Load 3D TIFF and process in memory
img = io.imread(args.input)
# Keep reference to before image if plotting
if args.p is not None:
before = np.copy(img[args.p])
verbose_print(args, f"Loaded image: {img.shape} {img.dtype}")
img = preprocess_image3d(args, img)
if args.p is not None:
after = np.copy(img[args.p])
else:
raise ValueError('Input is not a valid directory or file')
# Show A/B plot
if args.p is not None:
plt.subplot(121)
plt.imshow(before)
plt.title('Before')
plt.subplot(122)
plt.imshow(after)
plt.title('After')
plt.show()
verbose_print(args, f"Preprocessing done!")
def stack_main(args):
verbose_print(args, f'Stacking images in {args.input}')
paths, filenames = utils.tifs_in_dir(args.input)
verbose_print(args, f'Found {len(paths)} images')
img0 = io.imread(paths[0])
shape2d, dtype = img0.shape, img0.dtype
img = np.empty((len(paths), *shape2d), dtype)
for z, path in tqdm(enumerate(paths), total=len(paths)):
img[z] = io.imread(path)
io.imsave(args.output, img, compress=1)
verbose_print(args, f'Stacking done!')
def foreground_main(args):
verbose_print(args, f'Segmenting foreground from {args.input}')
# Load the input image
data = io.imread(args.input)
# Smoothing
if args.g is not None:
data = gaussian_blur(data, args.g).astype(data.dtype)
# Threshold image
foreground = (data > args.t) # .astype(np.uint8)
# Fill holes
# This is done slice-by-slice for now since there could be imaging problems where
# a part of a ventricle is actually in the image at z = 0 or z = -1
output = np.empty(foreground.shape, dtype=np.uint8)
for i, img in enumerate(foreground):
output[i] = binary_fill_holes(img)
output *= 255
# Save the result to TIFF
io.imsave(args.output, output, compress=3)
verbose_print(args, f'Segmentation written to {args.output}')
verbose_print(args, f'Foreground segmentation done!')
def preprocess_image2d(args, path, arr, i):
img = io.imread(path)
# Background removal
if args.t is not None:
# verbose_print(args, f"Performing background removal with threshold {args.t}")
img = remove_background(img, args.t)
# Histogram equalization
if args.k is not None:
if args.k == 0:
# verbose_print(args, f"Performing histogram equalization with default kernel size")
kernel_size = None
else:
# verbose_print(args, f"Performing histogram equalization with kernel size {args.k}")
kernel_size = args.k
img = equalize_adapthist(img, kernel_size=kernel_size)
# Convert to float (can't normalize based on single slice)
if args.float:
img = img_as_float32(img)
# verbose_print(args, f"Converted to normalized float32: min {img.min():.3f}, max {img.max():.3f}")
# Denoising
if args.s is not None:
# verbose_print(args, f"Performing noise removal with sigma {args.s} and wavelet {args.w}")
img = denoise2d(img, args.s, args.w)
arr[i] = img
def read_process_write(path, f, output, compress=3):
# Get output path from input name and output folder
filename = os.path.basename(path)
output_path = os.path.join(output, filename)
# Read image
img = io.imread(path)
# Process and write
process_write(img, f, output_path, compress)
def read_downsample_write(path, factor, output_dir, filename, compress=1):
arr = io.imread(path)
if isinstance(factor, int):
factors = tuple(factor for _ in range(arr.ndim))
else:
factors = tuple(factor)
data = downsample(arr, factors)
output = os.path.join(output_dir, filename)
io.imsave(output, data, compress=compress)
def rescale_image(path, threshold, max_val, output, filename, compress):
img = io.imread(path).astype(np.float32) # load image as float
# Subtract threshold and remove background by clipping negative values
img -= threshold
img = np.clip(img, 0, None)
# Divide by max_val (accounting for threshold) to scale to [0, 1]
img = img / (max_val - threshold)
# Save result
output_path = os.path.join(output, filename)
io.imsave(output_path, img, compress=compress)
def mesh_main(args):
if args.g is not None:
if len(args.g) == 1:
sigma = args.g[0]
else:
sigma = tuple(args.g)
if args.d is None:
downsample_factor = 1
else:
downsample_factor = np.asarray(args.d)
verbose_print(args, f'Meshing segmentation at {args.input}')
# Calculate the downsampled voxel size
voxel_orig = read_voxel_size(args.voxel_size)
voxel_down = tuple(voxel_orig * downsample_factor)
verbose_print(args, f'Original voxel size (um): {voxel_orig}')
verbose_print(args, f'Downsampled voxel size (um): {voxel_down}')
# Load segmentation
seg = io.imread(args.input)
# Smooth segmentation
if args.g is not None:
seg = smooth_segmentation(seg, sigma)
verbose_print(args, f'Smoothed segmentation with sigma {sigma}')
# Calculate mesh surface
verts, faces, normals, values = marching_cubes(seg, args.l, voxel_down,
args.s)
mesh = {
'verts': verts,
'faces': faces,
'normals': normals,
'values': values
}
verbose_print(args, f'Computed mesh with {len(normals)} normals')
# Plot mesh
if args.plot:
plot_mesh(mesh['verts'], mesh['faces'])
mlab.show()
# Save mesh
save_mesh(args.output, mesh)
verbose_print(args, f'Mesh saved to {args.output}')
verbose_print(args, 'Meshing done!')
def ventricle_main(args):
verbose_print(args, f'Segmenting ventricles in {args.input}')
# Load the input image
data = io.imread(args.input)
# Load the model
if args.model.endswith('.pt'):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")
model = load_model(args.model, device)
model = model.eval()
verbose_print(
args,
f'Pytorch model successfully loaded from {args.model} to {device} device'
)
# Segment the input image
verbose_print(args, f'Segmentation progress:')
output = segment_ventricles(model, data, args.t, device)
elif args.model.endswith('.h5'):
model = load_keras_model(args.model)
verbose_print(args,
f'Kerass model successfully loaded from {args.model}')
# Segment the input image
verbose_print(args, f'Segmentation progress:')
output = segment_ventricles_keras(model, data, args.t)
# Remove border regions
if args.exclude_border:
verbose_print(args, f'Removing regions connected to image border')
# This could also be done in 3D instead of slice-by-slice
# I'm not sure if images will start in ventricle, so doing slice-by-slice to be safe
img = np.zeros_like(output)
for i, data in tqdm(enumerate(output), total=len(output)):
img[i] = clear_border(data)
output = img
# Save the result to TIFF
io.imsave(args.output, output, compress=3)
verbose_print(args, f'Segmentation written to {args.output}')
verbose_print(args, f'Ventricle segmentation done!')
def convert_main(args):
nb_workers = _check_workers(args)
verbose_print(args, f"Converting {args.input} to Zarr")
# Find all TIFFs
paths, filenames = tifs_in_dir(args.input)
verbose_print(args, f"Found {len(paths)} TIFFs")
paths_chunked = [paths[pos:pos + args.c[0]] for pos in range(0, len(paths), args.c[0])]
img = io.imread(paths[0])
shape = (len(paths), *img.shape)
dtype = img.dtype
chunks = tuple(args.c)
arr = io.new_zarr(args.output, shape=shape, dtype=dtype, chunks=chunks)
verbose_print(args, f"Writiing to {args.output}")
args_list = []
for i, paths_batch in enumerate(paths_chunked):
args_list.append((paths_batch, i, chunks[0], arr))
with multiprocessing.Pool(nb_workers) as pool:
list(tqdm.tqdm(pool.imap(_convert_batch, args_list), total=len(args_list)))
verbose_print(args, f"Conversion done!")
def contrast_main(args):
# Initial setup
nb_workers = _check_workers(args)
if args.k is None:
verbose_print(args, f"Performing histogram equalization with default kernel size")
kernel_size = None
else:
verbose_print(args, f"Performing histogram equalization with kernel size {args.k}")
kernel_size = args.k
# Find all TIFFs
paths, filenames = tifs_in_dir(args.input)
verbose_print(args, f"Found {len(paths)} TIFFs")
# Make output folder
os.makedirs(args.output, exist_ok=True)
for path, filename in tqdm.tqdm(zip(paths, filenames), total=len(paths)):
img = io.imread(path)
adjusted = equalize_adapthist(img, kernel_size=kernel_size).astype(np.float32)
io.imsave(os.path.join(args.output, filename), adjusted, compress=args.c)
verbose_print(args, f"Contrast done!")
def test_imread():
img = io.imread('data/syto.tif')
assert img.shape == (64, 1024, 1024)
assert img.dtype == 'uint16'
assert img.max() == 4095
assert img.min() == 0
def image(image_path):
return io.imread(image_path)
def convert_batch(paths_batch, i, size, arr):
start = i * size
stop = start + len(paths_batch)
img = np.asarray([io.imread(path) for path in paths_batch])
arr[start:stop] = img
def estimate_histogram(paths, nbins=256):
image = np.asarray([io.imread(path) for path in tqdm.tqdm(paths)]) # Load images into 3D array
counts, bin_centers = histogram(image, nbins=nbins)
return counts, bin_centers
def wholeorg_features(args, features, gate_labels, niche_labels):
celltypes = read_csv(os.path.join(args.input, 'dataset/celltype_names.csv'))
niches = read_csv(os.path.join(args.input, 'dataset/niche_names.csv'))
if args.g is not None:
if len(args.g) == 1:
sigma = args.g[0]
else:
sigma = tuple(args.g)
if args.d is None:
downsample_factor = 1
else:
downsample_factor = np.asarray(args.d)
voxel_orig = read_voxel_size(os.path.join(args.input, 'dataset/voxel_size.csv'))
verbose_print(args, f'Original voxel size: {voxel_orig}')
voxel_down = tuple(voxel_orig * downsample_factor)
verbose_print(args, f'Downsampled voxel size: {voxel_down}')
# Overall organoid
foreground = io.imread(os.path.join(args.input, 'dataset/segment_foreground.tif'))
verbose_print(args, f'Loaded foreground segmentation: {foreground.shape}')
if not np.allclose(voxel_down, max(voxel_down)):
voxel_isotropic = tuple(max(voxel_down) * np.ones(len(voxel_down)))
verbose_print(args, f'Resampling foreground to isotropic: {voxel_isotropic}')
factors = np.asarray(voxel_isotropic) / np.asarray(voxel_down)
shape_isotropic = tuple([int(s / f) for s, f in zip(foreground.shape, factors)])
foreground = resize(foreground, output_shape=shape_isotropic, order=0).astype(foreground.dtype)
verbose_print(args, f'Resampled foreground segmentation: {foreground.shape}')
else:
voxel_isotropic = voxel_down
regions = regionprops(foreground)
# Find largest region
vol = 0
idx = None
for i, region in enumerate(regions):
if region.area > vol:
idx = i
largest = regions[idx]
volume_pixels = largest.area
eq_diam_pixels = largest.equivalent_diameter
major_axis_pixels = largest.major_axis_length
minor_axis_pixels = largest.minor_axis_length
volume_mm3 = volume_pixels * (np.asarray(voxel_isotropic) / 1000).prod()
eq_diam_mm = eq_diam_pixels * voxel_isotropic[0] / 1000
major_axis_mm = major_axis_pixels * voxel_isotropic[0] / 1000
minor_axis_mm = minor_axis_pixels * voxel_isotropic[0] / 1000
axis_ratio = major_axis_pixels / minor_axis_pixels
print(f'Organoid volume (mm3): {volume_mm3:.3f}')
print(f'Organoid equivalent diameter (mm): {eq_diam_mm:.3f}')
print(f'Organoid major axis length (mm): {major_axis_mm:.3f}')
print(f'Organoid minor axis length (mm): {minor_axis_mm:.3f}')
print(f'Organoid axis ratio: {axis_ratio:.3f}')
features[f'organoid volume (mm3)'] = volume_mm3
features[f'organoid equivalent diameter (mm)'] = eq_diam_mm
features[f'organoid major axis (mm)'] = major_axis_mm
features[f'organoid minor axis (mm)'] = minor_axis_mm
features[f'organoid axis ratio'] = axis_ratio
# Ventricles
ventricles = io.imread(os.path.join(args.input, 'dataset/segment_ventricles.tif'))
verbose_print(args, f'Loaded ventricle segmentation: {ventricles.shape}')
# Smooth segmentation
if args.g is not None:
ventricles = smooth_segmentation(ventricles, sigma) > 0.5
verbose_print(args, f'Smoothed segmentation with sigma {sigma}')
if not np.allclose(voxel_down, max(voxel_down)):
voxel_isotropic = tuple(max(voxel_down) * np.ones(len(voxel_down)))
verbose_print(args, f'Resampling ventricles to isotropic: {voxel_isotropic}')
factors = np.asarray(voxel_isotropic) / np.asarray(voxel_down)
shape_isotropic = tuple([int(s / f) for s, f in zip(ventricles.shape, factors)])
ventricles = resize(ventricles, output_shape=shape_isotropic, order=0, preserve_range=True).astype(
ventricles.dtype)
verbose_print(args, f'Resampled ventricle segmentation: {ventricles.shape}')
else:
voxel_isotropic = voxel_down
labels, nb_ventricles = label(ventricles)
verbose_print(args, f'Found {nb_ventricles} connected components in ventricle segmentation')
regions = regionprops(labels)
volumes_pixels = np.asarray([region.area for region in regions])
eq_diams_pixels = np.asarray([region.equivalent_diameter for region in regions])
major_axes_pixels = np.asarray([region.major_axis_length for region in regions])
minor_axes_pixels = np.asarray([region.minor_axis_length for region in regions])
volumes_um3 = volumes_pixels * np.asarray(voxel_isotropic).prod()
eq_diams_um = eq_diams_pixels * voxel_isotropic[0]
major_axes_um = major_axes_pixels * voxel_isotropic[0]
minor_axes_um = minor_axes_pixels * voxel_isotropic[0]
axis_ratios = major_axes_pixels / np.clip(minor_axes_pixels, 1, None)
ave_volume_um3 = volumes_um3.mean()
stdev_volume_um3 = volumes_um3.std()
ave_eq_diam_um = eq_diams_um.mean()
stdev_eq_diam_um = eq_diams_um.std()
ave_major_axis_um = major_axes_um.mean()
stdev_major_axis_um = major_axes_um.std()
ave_minor_axis_um = minor_axes_um.mean()
stdev_minor_axis_um = minor_axes_um.std()
ave_axis_ratio = axis_ratios.mean()
stdev_axis_ratio = axis_ratios.std()
print(f'ave. ventricle volume (um3): {ave_volume_um3:.3f} ({stdev_volume_um3:.3f})')
print(f'ave. ventricle equivalent diameter (um): {ave_eq_diam_um:.3f} ({stdev_eq_diam_um:.3f})')
print(f'ave. ventricle major axis length (um): {ave_major_axis_um:.3f} ({stdev_major_axis_um:.3f})')
print(f'ave. ventricle minor axis length (mm): {ave_minor_axis_um:.3f} ({stdev_minor_axis_um:.3f})')
print(f'ave. ventricle axis ratio: {ave_axis_ratio:.3f} ({stdev_axis_ratio:.3f})')
features[f'ventricle count'] = nb_ventricles
features[f'ventricle volume mean (um3)'] = ave_volume_um3
features[f'ventricle volume stdev (um3)'] = stdev_volume_um3
features[f'ventricle equivalent diameter mean (um)'] = ave_eq_diam_um
features[f'ventricle equivalent diameter stdev (um)'] = stdev_eq_diam_um
features[f'ventricle major axis mean (um)'] = ave_major_axis_um
features[f'ventricle major axis stdev (um)'] = stdev_major_axis_um
features[f'ventricle minor axis mean (um)'] = ave_minor_axis_um
features[f'ventricle minor axis stdev (um)'] = stdev_minor_axis_um
features[f'ventricle axis ratio mean'] = ave_axis_ratio
features[f'ventricle axis ratio stdev'] = stdev_axis_ratio
# Distance to surface
mask = foreground > 0
verbose_print(args, f'Made foreground mask: {mask.shape}')
# Find surface coordinates
eroded = binary_erosion(mask)
surface = np.logical_and(mask, np.logical_not(eroded))
coords = np.asarray(np.where(surface)).T
surface_points = coords * np.asarray(voxel_down)
# Load cell centers
centroids_um = np.load(os.path.join(args.input, 'dataset/centroids_um.npy'))
# Query nearest surface point for each cell center
print('Surface distances')
nbrs = NearestNeighbors(n_neighbors=1).fit(surface_points)
for n, niche_name in enumerate(niches):
print('neighborhood', n)
niche_idx = np.where(niche_labels == n)[0] # This is an index into centoids_um
niche_centroids_um = centroids_um[niche_idx]
gate_labels_niche = gate_labels[niche_idx]
for c, celltype_name in enumerate(celltypes):
print('cell type', c)
celltype_idx = np.where(gate_labels_niche[:, c] == 1)[0]
if len(celltype_idx) > 0:
niche_celltype_centroids_um = niche_centroids_um[celltype_idx]
surface_dist, _ = nbrs.kneighbors(niche_celltype_centroids_um)
ave_surface_dist = surface_dist.mean()
stdev_surface_dist = surface_dist.std()
else:
ave_surface_dist = np.nan
stdev_surface_dist = np.nan
print(f'Ave. surface dist: {ave_surface_dist:.3f} ({stdev_surface_dist:.3f}, n = {len(celltype_idx)})')
features[f'{niche_name} nbrhd, {celltype_name} surface distance mean (um)'] = ave_surface_dist
features[f'{niche_name} nbrhd, {celltype_name} surface distance stdev (um)'] = stdev_surface_dist
return features
