From 5f15a6651e188c97e17387fc9476f3fe35a8014b Mon Sep 17 00:00:00 2001 From: Constantin Pape Date: Mon, 2 Dec 2024 22:14:29 +0100 Subject: [PATCH] Doc updates (#71) Update documentation and function doc strings --- README.md | 25 ++-- doc/images/synapse-reconstruction.png | Bin 0 -> 923401 bytes doc/start_page.md | 59 +++++++++- .../qualitative_evaluation.py | 9 +- scripts/inner_ear/processing/run_analyis.py | 6 +- .../distance_measurements.py | 110 ++++++++++++++---- synaptic_reconstruction/file_utils.py | 14 ++- .../ground_truth/.gitignore | 1 + .../ground_truth/matching.py | 16 ++- .../ground_truth/region_extraction.py | 106 ----------------- .../ground_truth/shape_refinement.py | 5 +- synaptic_reconstruction/imod/export.py | 88 +++++++++++--- synaptic_reconstruction/imod/to_imod.py | 6 +- synaptic_reconstruction/inference/actin.py | 3 +- .../inference/active_zone.py | 3 +- .../inference/compartments.py | 3 +- synaptic_reconstruction/inference/cristae.py | 3 +- .../inference/mitochondria.py | 3 +- synaptic_reconstruction/inference/util.py | 75 ++++-------- synaptic_reconstruction/inference/vesicles.py | 3 +- synaptic_reconstruction/morphology.py | 79 ++++++++----- .../training/domain_adaptation.py | 11 +- .../training/semisupervised_training.py | 6 +- .../training/supervised_training.py | 11 +- 24 files changed, 365 insertions(+), 280 deletions(-) create mode 100644 doc/images/synapse-reconstruction.png create mode 100644 synaptic_reconstruction/ground_truth/.gitignore delete mode 100644 synaptic_reconstruction/ground_truth/region_extraction.py diff --git a/README.md b/README.md index 1a8d937..2af858d 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,10 @@ -# Synaptic Reconstruction +# SynapseNet: Deep Learning for Automatic Synapse Reconstruction -Reconstruction of synaptic structures in electron microscopy. +SynapseNet is a tool for segmentation and analysis of synapses in electron microscopy. -THIS IS WORK IN PROGRESS! +To learn how to use SynapseNet, check out [the documentation](https://computational-cell-analytics.github.io/synapse-net/). +To learn more about how it works, check out [our preprint](TODO). -## Installation - -- Make sure conda or mamba is installed. - - If you don't have a conda installation yet we recommend [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html) -- Create the environment with all required dependencies: `mamba env create -f environment.yaml` -- Activate the environment: `mamba activate synaptic-reconstruction` -- Install the package: `pip install -e .` - -## Tools - -### Segmentation Correction - -https://napari.org/stable/howtos/layers/labels.html - -### Distance Measurements +See an example reconstruction of a mossy fibre synapse with SynapseNet. +Automatic segmentation of synaptic vesicles are rendered in orange, active zones in blue and two mitochondria in red and cyan. +![Reconstruction of a mossy fiber synapse](doc/images/synapse-reconstruction.png) diff --git a/doc/images/synapse-reconstruction.png b/doc/images/synapse-reconstruction.png new file mode 100644 index 0000000000000000000000000000000000000000..b7cf05843a1cf42c692bc637a618dfe27e6a49f4 GIT binary patch literal 923401 zcmdSAg;yI<+qa9mHBelOLy_X{PH}fArBI+qfa31O3xVQR+>3_bUff-RI|O$)eCvGg z^YlIc!O5DeBr}uQBYWR+-M<~Ft}2g>L5cwf2ZybwAfpKfhm;NnhbWAO^19~uV}kGN z2a=1FqBh#=%Lnaq#OrT#Cj|o+I5^6#|MP}-7T5<*=(@*l=#mv!km{lYySRoi0-Cuzve&x+s-oI zM|-aSw$aV$K*qpF@^34R*!b!H?hw5c_W#e*XdmYZ>DXM}ujSN@`d?SDtl?v7qb4!# zzMU_M9()Y*j+1)lRWvrK&(*Y&bKMg@Z7S3((jbuH%-==@?Rc5>moFf{GEOBNC80+^%nQ&!|(Eoa1o3YM5 zSYponu^ zqYxga6F|=+rx>oJIC(SL{&Ko+{V7&U#|yAXt?R#iCfc3h7#%qP)z_=d7f(}hV9Wdq z4q+{jS~l3p6=}zj8qJN~LaWj5gd!*cahEZd8BX*Yv`VTTA#sXR6xWZVuCG&a+v{*i zyN~F+8^YJMf0n((*-}v*;Pg+&JwY>387I`cTVAodd_3pLpbWdnhhB6`5&obT5XPac zGtCX z&+cEf|MdK5;h^B=VOa#uY4ml@t@A-07QJl;mgF46P?quZpv`<%sD`i+;e3m={Ae!AQ?BzTIn1w+9ufxHxa7P}agJjI zS1dA`g_U?RrC5BSWZ1o0bvJ#!i|tZ4J1qwa9HXRI~0ZSS=7mK^1}fjBq}iCEAqLa#lZU^M5ViW3&f3 z+I^>UK-%_GdGeYM8sP;XwC$$!&(BX5^R+l!$hUt#d~2+b|D;j>eRFfh7Ix|UYiQ3@ zMjD11C*@6)q*Q&1<{`={f*#JHLshy}{9Iwok5Y zpb)#r>52J>^Sog8Vyt$|7BhzE9iAhIG8M&A1hL*5{MP1=$D$MJLq!(o`n?UdP>{LySFnTT@A@Wp0OqH4!aHJ~0P2vqaP6(4TR z#1ZAkI$mJC{5NT9C;!Wfu(yuD#=RmbQoBYPPrnAr5EyE)bR+DYhMD=}J@@jjExY)Z z)mo#SBi~+1JAEO3zFeAHYv4xj{UbEE{bQWCd3MFs6X%^I8Y=X!9_VhGJXl6pJ=BNL zY5LfK9ot&|^6qpMBOR57c-q;glmGC?nxHXocH4BzS^ z#n7e6T;grjBbPCYXeiq>?MtGnzz9rL1T)S97fNIT*LpqUZ(h z)q~r;2;~abYjKOC?YOYt@S`47ycekNU9b5zSch?I{#j1Sr~wW-M%oEqIzR)0MC5Vq)GwmKOr z<}vmK#@D^gEC3`pS%91P4Qzhy%!gU81tbzVj_tOtR1g0*NZn*p#MU2{FbeLLL4!_;!eBfE~cWZ?t9@n-LvZfwFP6YKs#G;KWIJaJG~Qo8l_ z_ZwxItJPHJ;~TI%58oNnrK?nnvTfm4YY)~+gG-YRf^$IrK$-m=Jsttm7y&-+dV&2EV82Af6O{k~WysIYZo)}WB+(=`yco-=Dj4c@b9Ot-SpG9E5XS}#mPz!u{ zd>?`c+xMo!5v|kc(kg_DRoixg+*d6>w~P313x**MnwIKBeSb^Rf#h(E;)*$RAmMD7Og)mxrtQeLr=43RxaI9EsQvR-v^Qp z$CPcI9)+x$E#VZ&S)9T}tC}oekKz5BC%8uksHvuWGGGUx&ME(H;w-Vx>eLm@uv(B9 z%TiBG>_-geU=e3Bn)a@}0K_z>Cw208M}1PRUYH=lQyWKPr zpu~WjI3~|e693G%_DmkPMZblr`I}YdCALAW?o> zGzm+6SbupW^LS<=7qUJnFzWqpz7yR@>{mtk&Dl-l!t?T?LgpPpb`vJwl@vAHjYAMs zD~sfp(|DwayOy31n*r?Y?^Vw1m`jM^6VE#%*Aa|47dlF<=*`a0ca3~v6!9-l)@ zN3r0`rj8}<9%-yU2WtYIIkRNJxL z`r^R?s8))2zXAD>ebmbpoSI2!+6QNa@L?w-2R|CD(N5M1Z@S^<=dlk_I*7uw(PkU= zZ~bsZ{wT5+ld|X>DM|`7(Os9z7#TYh|Mfh^kCku{4w06CFJ@`{D)@|2Oh_|{V%ms= zlk=hc;rmxP1a)~SlVek=DpkRSQd1ZxCkgQpFS_e7sCnx6XzC8PUQ)VfRh;|%n?Df6 z*upbGy)6tSYNQw!x+d)}U{WzZq73rjOhf=*)k(4SGWkCYVih3-5%5W@*~m&PHK^I+ zuBBQ(J_nY?23!ky1n_YoM`}i*Z4J#O>#i_sI`r4%nB{fv{g#QY)K%!oX~zsgx4P7h zx?cb!-a0#fQ4~PCTMrM1dZR+?T^vWj4EezImbxFCRfhr3IJXry-8*8h36?_s;U~i+ zYWJ%2i&vXG$OaAS9oV6W!~?lgiVxiTUAFze?~uCHnWg!#179Ofz_-Fl zfO0MJig%TK-fZvJ`44cNy2Tz_>MrCX2~YipxVQ=)8T#=~skYN+ zkhHqf6L`1w*Nvkgh|0OA{2TQaZVTRbE$Awa1pn*i@$f4RGl918n3LCsLo&ETnb9>E z`{ZJTJu^PHhBDEU(XnF3{QBunDjRuh(x2}%)l&KYNLJs_cyedP! 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No newline at end of file +# SynapseNet: Deep Learning for Automatic Synapse Reconstruction + +SynapseNet is a tool for automatic segmentation and analysis of synapses in electron micrographs. +It provides deep neural networks for: +- Synaptic vesicle segmentation in ssTEM (2d data) and (cryo-)electron tomography (3d data) +- Active zone membrane segmentation in electron tomography +- Mitochondrion segmentation in electron tomography +- Synaptic compartment segmentation in electron tomography +- Synaptic ribbon and pre-synaptic density segmentation for ribbon synapses in electron tomography +It also offers functionality for quantifying synaptic ultrastructure based on segmentation results, for example by measuring vesicle or structure morphology, measuring distances between vesicles and structures, or assigning vesicles into different pools. +SynapseNet mainly targets electron tomography, but can also be appled to other types of electron microscopy, +especially throught the [domain adaptation](domain-adaptation) functionality. + +SynapseNet offers a [napari plugin](napari-plugin), [command line interface](command-line-interface), and [python library](python-library). +Please cite our [bioRxiv preprint](TODO) if you use it in your research. + +**The rest of the documentation will be updated in the next days!** + +## Requirements & Installation + +- Requirements: Tested on Linux but should work on Mac/Windows. + - GPU needed to use 3d segmentation networks +- Installation via conda and local pip install + - GPU support + +- Make sure conda or mamba is installed. + - If you don't have a conda installation yet we recommend [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html) +- Create the environment with all required dependencies: `mamba env create -f environment.yaml` +- Activate the environment: `mamba activate synaptic-reconstruction` +- Install the package: `pip install -e .` + +## Napari Plugin + +lorem ipsum + +## Command Line Functionality + +- segmentation cli +- export to imod + - vesicles / spheres + - objects + +## Python Library + +- segmentation functions +- distance and morphology measurements +- imod + +### Domain Adaptation + +- explain domain adaptation +- link to the example script + +### Network Training + +- explain / diff to domain adaptation +- link to the example script diff --git a/scripts/cooper/full_reconstruction/qualitative_evaluation.py b/scripts/cooper/full_reconstruction/qualitative_evaluation.py index 546d8f0..7cd8c20 100644 --- a/scripts/cooper/full_reconstruction/qualitative_evaluation.py +++ b/scripts/cooper/full_reconstruction/qualitative_evaluation.py @@ -75,6 +75,7 @@ def eval_compartments(): print("Compartment Evaluation:") print("Avergage pieces per compartment:", avg, "+-", std) print("Max pieces per compartment:", max_) + print("Number of compartments:", len(pieces_per_compartment)) def eval_mitos(): @@ -124,11 +125,11 @@ def check_mitos(): def main(): # check_mitos() - eval_mitos() - print() + # eval_mitos() + # print() eval_compartments() - print() - eval_az() + # print() + # eval_az() main() diff --git a/scripts/inner_ear/processing/run_analyis.py b/scripts/inner_ear/processing/run_analyis.py index ca5ea0b..fbb00f1 100644 --- a/scripts/inner_ear/processing/run_analyis.py +++ b/scripts/inner_ear/processing/run_analyis.py @@ -10,8 +10,9 @@ from synaptic_reconstruction.file_utils import get_data_path from synaptic_reconstruction.distance_measurements import ( - measure_segmentation_to_object_distances, filter_blocked_segmentation_to_object_distances, + load_distances, + measure_segmentation_to_object_distances, ) from synaptic_reconstruction.morphology import compute_radii, compute_object_morphology @@ -172,8 +173,9 @@ def load_dist(measurement_path, seg_ids=None): # Filter out the blocked vesicles. if apply_extra_filters: + rav_dists, ep1, ep2, all_rav_ids = load_distances(distance_paths["ribbon"]) rav_ids = filter_blocked_segmentation_to_object_distances( - vesicles, distance_paths["ribbon"], seg_ids=rav_ids, line_dilation=4, verbose=True, + vesicles, rav_dists, ep1, ep2, all_rav_ids, filter_seg_ids=rav_ids, line_dilation=4, verbose=True, ) rav_ids = filter_border_vesicles(vesicles, seg_ids=rav_ids) diff --git a/synaptic_reconstruction/distance_measurements.py b/synaptic_reconstruction/distance_measurements.py index cbbc48c..4c25691 100644 --- a/synaptic_reconstruction/distance_measurements.py +++ b/synaptic_reconstruction/distance_measurements.py @@ -1,6 +1,6 @@ import os import multiprocessing as mp -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Union import numpy as np @@ -18,7 +18,25 @@ skfmm = None -def compute_geodesic_distances(segmentation, distance_to, resolution=None, unsigned=True): +def compute_geodesic_distances( + segmentation: np.ndarray, + distance_to: np.ndarray, + resolution: Optional[Union[int, float, Tuple[int, int, int]]] = None, + unsigned: bool = True, +) -> np.ndarray: + """Compute the geodesic distances between a segmentation and a distance target. + + This function require scikit-fmm to be installed. + + Args: + segmentation: The binary segmentation. + distance_to: The binary distance target. + resolution: The voxel size of the data, used to scale the distances. + unsigned: Whether to return the unsigned or signed distances. + + Returns: + Array with the geodesic distance values. + """ assert skfmm is not None, "Please install scikit-fmm to use compute_geodesic_distance." invalid = segmentation == 0 @@ -43,14 +61,12 @@ def compute_geodesic_distances(segmentation, distance_to, resolution=None, unsig return distances -# TODO update this def _compute_centroid_distances(segmentation, resolution, n_neighbors): - # TODO enable eccentricity centers instead props = regionprops(segmentation) centroids = np.array([prop.centroid for prop in props]) if resolution is not None: - pass # TODO scale the centroids - + scale_factor = np.array(resolution)[:, None] + centroids *= scale_factor pair_distances = pairwise_distances(centroids) return pair_distances @@ -313,11 +329,13 @@ def create_pairwise_distance_lines( endpoints1: One set of distance end points. endpoints2: The other set of distance end points. seg_ids: The segmentation pair corresponding to each distance. - n_neighbors: ... - pairs: ... - bb: .... - scale: ... - remove_duplicates: ... + n_neighbors: The number of nearest neighbors to take into consideration + for creating the distance lines. + pairs: Optional list of ids to use for creating the distance lines. + bb: Bounding box for restricing the distance line creation. + scale: Scale factor for resizing the distance lines. + Use this if the corresponding segmentations were downscaled for visualization. + remove_duplicates: Remove duplicate id pairs from the distance lines. Returns: The lines for plotting in napari. @@ -386,8 +404,10 @@ def create_object_distance_lines( endpoints1: One set of distance end points. endpoints2: The other set of distance end points. seg_ids: The segmentation ids corresponding to each distance. - max_distance: ... - scale: ... + max_distance: Maximal distance for drawing the distance line. + filter_seg_ids: Segmentation ids to restrict the distance lines. + scale: Scale factor for resizing the distance lines. + Use this if the corresponding segmentations were downscaled for visualization. Returns: The lines for plotting in napari. @@ -416,13 +436,32 @@ def create_object_distance_lines( return lines, properties -def keep_direct_distances(segmentation, measurement_path, line_dilation=0, scale=None): - """Filter out all distances that are not direct. - I.e. distances that cross another segmented object. - """ +def keep_direct_distances( + segmentation: np.ndarray, + distances: np.ndarray, + endpoints1: np.ndarray, + endpoints2: np.ndarray, + seg_ids: np.ndarray, + line_dilation: int = 0, + scale: Optional[Tuple[int, int, int]] = None, +) -> List[List[int, int]]: + """Filter out all distances that are not direct; distances that are occluded by another segmented object. - distances, ep1, ep2, seg_ids = load_distances(measurement_path) - distance_lines, properties = create_object_distance_lines(distances, ep1, ep2, seg_ids, scale=scale) + Args: + segmentation: The segmentation from which the distances are derived. + distances: The measurd distances. + endpoints1: One set of distance end points. + endpoints2: The other set of distance end points. + seg_ids: The segmentation ids corresponding to each distance. + line_dilation: Dilation factor of the distance lines for determining occlusions. + scale: Scaling factor of the segmentation compared to the distance measurements. + + Returns: + The list of id pairs that are kept. + """ + distance_lines, properties = create_object_distance_lines( + distances, endpoints1, endpoints2, seg_ids, scale=scale + ) ids_a, ids_b = properties["id_a"], properties["id_b"] filtered_ids_a, filtered_ids_b = [], [] @@ -459,10 +498,35 @@ def keep_direct_distances(segmentation, measurement_path, line_dilation=0, scale def filter_blocked_segmentation_to_object_distances( - segmentation, measurement_path, line_dilation=0, scale=None, seg_ids=None, verbose=False, -): - distances, ep1, ep2, seg_ids = load_distances(measurement_path) - distance_lines, properties = create_object_distance_lines(distances, ep1, ep2, seg_ids, scale=scale) + segmentation: np.ndarray, + distances: np.ndarray, + endpoints1: np.ndarray, + endpoints2: np.ndarray, + seg_ids: np.ndarray, + line_dilation: int = 0, + scale: Optional[Tuple[int, int, int]] = None, + filter_seg_ids: Optional[List[int]] = None, + verbose: bool = False, +) -> List[int]: + """Filter out all distances that are not direct; distances that are occluded by another segmented object. + + Args: + segmentation: The segmentation from which the distances are derived. + distances: The measurd distances. + endpoints1: One set of distance end points. + endpoints2: The other set of distance end points. + seg_ids: The segmentation ids corresponding to each distance. + line_dilation: Dilation factor of the distance lines for determining occlusions. + scale: Scaling factor of the segmentation compared to the distance measurements. + filter_seg_ids: Segmentation ids to restrict the distance lines. + verbose: Whether to print progressbar. + + Returns: + The list of id pairs that are kept. + """ + distance_lines, properties = create_object_distance_lines( + distances, endpoints1, endpoints2, seg_ids, scale=scale + ) all_seg_ids = properties["id"] filtered_ids = [] diff --git a/synaptic_reconstruction/file_utils.py b/synaptic_reconstruction/file_utils.py index fc572c8..d88a31d 100644 --- a/synaptic_reconstruction/file_utils.py +++ b/synaptic_reconstruction/file_utils.py @@ -1,7 +1,17 @@ import os +from typing import List, Optional, Union -def get_data_path(folder, n_tomograms=1): +def get_data_path(folder: str, n_tomograms: Optional[int] = 1) -> Union[str, List[str]]: + """Get the path to all tomograms stored as .rec or .mrc files in a folder. + + Args: + folder: The folder with tomograms. + n_tomograms: The expected number of tomograms. + + Returns: + The filepath or list of filepaths of the tomograms in the folder. + """ file_names = os.listdir(folder) tomograms = [] for fname in file_names: @@ -11,7 +21,5 @@ def get_data_path(folder, n_tomograms=1): if n_tomograms is None: return tomograms - assert len(tomograms) == n_tomograms, f"{folder}: {len(tomograms)}, {n_tomograms}" - return tomograms[0] if n_tomograms == 1 else tomograms diff --git a/synaptic_reconstruction/ground_truth/.gitignore b/synaptic_reconstruction/ground_truth/.gitignore new file mode 100644 index 0000000..5c4b094 --- /dev/null +++ b/synaptic_reconstruction/ground_truth/.gitignore @@ -0,0 +1 @@ +edge_filter.py diff --git a/synaptic_reconstruction/ground_truth/matching.py b/synaptic_reconstruction/ground_truth/matching.py index ecfa88f..f085ee5 100644 --- a/synaptic_reconstruction/ground_truth/matching.py +++ b/synaptic_reconstruction/ground_truth/matching.py @@ -4,7 +4,21 @@ from skimage.segmentation import relabel_sequential -def find_additional_objects(ground_truth, segmentation, matching_threshold=0.5): +def find_additional_objects( + ground_truth: np.ndarray, + segmentation: np.ndarray, + matching_threshold: float = 0.5 +) -> np.ndarray: + """Compare ground-truth annotations with a segmentation to find objects not in the annotation. + + Args: + ground_trut: + segmentation: + matching_threshold: + + Returns: + """ + segmentation = relabel_sequential(segmentation)[0] # Match the objects in the segmentation to the ground-truth. diff --git a/synaptic_reconstruction/ground_truth/region_extraction.py b/synaptic_reconstruction/ground_truth/region_extraction.py deleted file mode 100644 index c246b0d..0000000 --- a/synaptic_reconstruction/ground_truth/region_extraction.py +++ /dev/null @@ -1,106 +0,0 @@ -from typing import Optional - -import numpy as np -from sklearn.decomposition import PCA -from scipy.ndimage import affine_transform - - -def rotate_3d_array(arr, rotation_matrix, center, order): - # Translate the array to center it at the origin - translation_to_origin = np.eye(4) - translation_to_origin[:3, 3] = -center - - # Translation back to original position - translation_back = np.eye(4) - translation_back[:3, 3] = center - - # Construct the full transformation matrix: Translation -> Rotation -> Translation back - transformation_matrix = np.eye(4) - transformation_matrix[:3, :3] = rotation_matrix # Apply the PCA rotation - - # Combine the transformations: T_back * R * T_origin - full_transformation = translation_back @ transformation_matrix @ translation_to_origin - - # Apply affine_transform (we extract the 3x3 rotation matrix and the translation vector) - rotated_arr = affine_transform( - arr, - full_transformation[:3, :3], # Rotation part - offset=full_transformation[:3, 3], # Translation part - output_shape=arr.shape, # Keep output shape the same - order=order - ) - return rotated_arr - - -# Find the rotation that aligns the data with the PCA -def _find_rotation(segmentation): - foreground_coords = np.argwhere(segmentation > 0) - - pca = PCA(n_components=3) - pca.fit(foreground_coords) - - rotation_matrix = pca.components_ - - return rotation_matrix - - -def extract_and_align_foreground( - segmentation: np.ndarray, - raw: Optional[np.ndarray] = None, - extract_bb: bool = True, -): - """Extract and align the bounding box containing foreground from the segmentation. - - This function will find the closest fitting, non-axis-aligned rectangular bounding box - that contains the segmentation foreground. It will then rotate the data, so that it is - axis-aligned. - - Args: - segmentation: The input segmentation. - raw: The raw data. - extract_bb: Whether to cout out the bounding box. - - Returns: - TODO - """ - rotation_matrix = _find_rotation(segmentation) - - # Calculate the center of the original array. - center = np.array(segmentation.shape) / 2.0 - - # Rotate the array. - segmentation = rotate_3d_array(segmentation, rotation_matrix, center, order=0) - - if extract_bb: - bb = np.where(segmentation != 0) - bb = tuple( - slice(int(b.min()), int(b.max()) + 1) for b in bb - ) - else: - bb = np.s_[:] - - if raw is not None: - raw = rotate_3d_array(raw, rotation_matrix, center, order=1) - - if raw is not None: - return segmentation[bb], raw[bb] - - return segmentation[bb] - - -if __name__ == "__main__": - import h5py - import napari - - segmentation_path = "tomogram-000.h5" - - with h5py.File(segmentation_path, "r") as f: - raw = f["/raw"][:] - segmentation = f["/labels/vesicles"][:] - - segmentation, raw = extract_and_align_foreground(segmentation, raw) - - v = napari.Viewer() - v.add_image(raw) - v.add_labels(segmentation) - napari.run() diff --git a/synaptic_reconstruction/ground_truth/shape_refinement.py b/synaptic_reconstruction/ground_truth/shape_refinement.py index 96d1f2a..8c357ae 100644 --- a/synaptic_reconstruction/ground_truth/shape_refinement.py +++ b/synaptic_reconstruction/ground_truth/shape_refinement.py @@ -52,8 +52,9 @@ def edge_filter( - "sato": Edges are found with a sato-filter, followed by smoothing and leveling. per_slice: Compute the filter per slice instead of for the whole volume. n_threads: Number of threads for parallel computation over the slices. + Returns: - Volume with edge strength. + Edge filter response. """ if method not in FILTERS: raise ValueError(f"Invalid edge filter method: {method}. Expect one of {FILTERS}.") @@ -100,6 +101,7 @@ def check_filters( The filter names must match `method` in `edge_filter`. sigmas: The sigma values to use for the filters. show: Whether to show the filter responses in napari. + Returns: Dictionary with the filter responses. """ @@ -153,6 +155,7 @@ def refine_vesicle_shapes( return_seeds: Whether to return the seeds used for the watershed. compactness: The compactness parameter passed to the watershed function. Higher compactness leads to more regular sized vesicles. + Returns: The refined vesicles. """ diff --git a/synaptic_reconstruction/imod/export.py b/synaptic_reconstruction/imod/export.py index 0607484..5457bf9 100644 --- a/synaptic_reconstruction/imod/export.py +++ b/synaptic_reconstruction/imod/export.py @@ -1,7 +1,7 @@ import shutil import tempfile from subprocess import run -from typing import Dict, Optional +from typing import Dict, List, Optional, Tuple import imageio.v3 as imageio import numpy as np @@ -144,7 +144,23 @@ def export_segmentation( imageio.imwrite(output_path, segmentation.astype("uint8"), compression="zlib") -def draw_spheres(coordinates, radii, shape, verbose=True): +def draw_spheres( + coordinates: np.ndarray, + radii: np.ndarray, + shape: Tuple[int, int, int], + verbose: bool = True, +) -> np.ndarray: + """Create a volumetric segmentation by painting spheres around the given coordinates. + + Args: + coordinates: The center coordinates of the spheres. + radii: The radii of the spheres. + shape: The shape of the volume. + verbose: Whether to print the progress bar. + + Returns: + The segmentation volume with painted spheres. + """ labels = np.zeros(shape, dtype="uint32") for label_id, (coord, radius) in tqdm( enumerate(zip(coordinates, radii), start=1), total=len(coordinates), disable=not verbose @@ -166,10 +182,35 @@ def draw_spheres(coordinates, radii, shape, verbose=True): def load_points_from_imodinfo( - imod_path, full_shape, bb=None, - exclude_labels=None, exclude_label_patterns=None, - resolution=None, -): + imod_path: str, + full_shape: Tuple[int, int, int], + bb: Optional[Tuple[slice, slice, slice]] = None, + exclude_labels: Optional[List[int]] = None, + exclude_label_patterns: Optional[List[str]] = None, + resolution: Optional[float] = None, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Dict[int, str]]: + """Load point coordinates, radii and label information from a .mod file. + + The coordinates and sizes returned will be scaled so that they are in + the voxel coordinate space if the 'resolution' parameter is passed. + If it is not passed then the radius will be returned in the physical resolution. + + Args: + imod_path: The filepath to the .mod file. + full_shape: The voxel shape of the volume. + bb: Optional bounding box to limit the extracted points to. + exclude_labels: Label ids to exclude from the export. + This can be used to exclude specific labels / classes, specifying them by their id. + exclude_label_patterns: Label names to exclude from the export. + This can be used to exclude specific labels / classes, specifying them by their name. + resolution: The resolution / voxel size of the data. Will be used to scale the radii. + + Returns: + The center coordinates of the sphere annotations. + The radii of the spheres. + The ids of the semantic labels. + The names of the semantic labels. + """ coordinates, sizes, labels = [], [], [] label_names = {} @@ -274,14 +315,33 @@ def load_points_from_imodinfo( def export_point_annotations( - imod_path, - shape, - bb=None, - exclude_labels=None, - exclude_label_patterns=None, - return_coords_and_radii=False, - resolution=None, -): + imod_path: str, + shape: Tuple[int, int, int], + bb: Optional[Tuple[slice, slice, slice]] = None, + exclude_labels: Optional[List[int]] = None, + exclude_label_patterns: Optional[List[str]] = None, + return_coords_and_radii: bool = False, + resolution: Optional[float] = None, +) -> Tuple[np.ndarray, np.ndarray, Dict[int, str]]: + """Create a segmentation by drawing spheres corresponding to objects from a .mod file. + + Args: + imod_path: The filepath to the .mod file. + shape: The voxel shape of the volume. + bb: Optional bounding box to limit the extracted points to. + exclude_labels: Label ids to exclude from the segmentation. + This can be used to exclude specific labels / classes, specifying them by their id. + exclude_label_patterns: Label names to exclude from the segmentation. + This can be used to exclude specific labels / classes, specifying them by their name. + return_coords_and_radii: Whether to also return the underlying coordinates + and radii of the exported spheres. + resolution: The resolution / voxel size of the data. Will be used to scale the radii. + + Returns: + The exported segmentation. + The label ids for the instance ids in the segmentation. + The map of label ids to corresponding obejct names. + """ coordinates, radii, labels, label_names = load_points_from_imodinfo( imod_path, shape, bb=bb, exclude_labels=exclude_labels, diff --git a/synaptic_reconstruction/imod/to_imod.py b/synaptic_reconstruction/imod/to_imod.py index 5103d31..b2084ed 100644 --- a/synaptic_reconstruction/imod/to_imod.py +++ b/synaptic_reconstruction/imod/to_imod.py @@ -38,7 +38,7 @@ def write_segmentation_to_imod( output_path: str, segmentation_key: Optional[str] = None, ) -> None: - """Write a segmentation to a mod file as closed contour objects. + """Write a segmentation to a mod file as closed contour object(s). Args: mrc_path: The filepath to the mrc file from which the segmentation was derived. @@ -109,8 +109,8 @@ def convert_segmentation_to_spheres( props: Optional list of regionprops Returns: - np.array: the center coordinates - np.array: the radii + The center coordinates. + The radii. """ num_workers = multiprocessing.cpu_count() if num_workers is None else num_workers if props is None: diff --git a/synaptic_reconstruction/inference/actin.py b/synaptic_reconstruction/inference/actin.py index afc6e54..ff4ed17 100644 --- a/synaptic_reconstruction/inference/actin.py +++ b/synaptic_reconstruction/inference/actin.py @@ -24,8 +24,7 @@ def segment_actin( scale: Optional[List[float]] = None, mask: Optional[np.ndarray] = None, ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: - """ - Segment actin in an input volume. + """Segment actin in an input volume. Args: input_volume: The input volume to segment. diff --git a/synaptic_reconstruction/inference/active_zone.py b/synaptic_reconstruction/inference/active_zone.py index d611693..216deb9 100644 --- a/synaptic_reconstruction/inference/active_zone.py +++ b/synaptic_reconstruction/inference/active_zone.py @@ -10,8 +10,7 @@ def find_intersection_boundary(segmented_AZ: np.ndarray, segmented_compartment: np.ndarray) -> np.ndarray: - """ - Find the cumulative intersection of the boundary of each label in segmented_compartment with segmented_AZ. + """Find the intersection of the boundaries of each objects in segmented_compartment with segmented_AZ. Args: segmented_AZ: 3D array representing the active zone (AZ). diff --git a/synaptic_reconstruction/inference/compartments.py b/synaptic_reconstruction/inference/compartments.py index a822d9f..dd6adf7 100644 --- a/synaptic_reconstruction/inference/compartments.py +++ b/synaptic_reconstruction/inference/compartments.py @@ -157,8 +157,7 @@ def segment_compartments( n_slices_exclude: int = 0, **kwargs, ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: - """ - Segment synaptic compartments in an input volume. + """Segment synaptic compartments in an input volume. Args: input_volume: The input volume to segment. diff --git a/synaptic_reconstruction/inference/cristae.py b/synaptic_reconstruction/inference/cristae.py index 467bfd9..ce62dcb 100644 --- a/synaptic_reconstruction/inference/cristae.py +++ b/synaptic_reconstruction/inference/cristae.py @@ -43,8 +43,7 @@ def segment_cristae( scale: Optional[List[float]] = None, mask: Optional[np.ndarray] = None, ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: - """ - Segment cristae in an input volume. + """Segment cristae in an input volume. Args: input_volume: The input volume to segment. Expects 2 3D volumes: raw and mitochondria diff --git a/synaptic_reconstruction/inference/mitochondria.py b/synaptic_reconstruction/inference/mitochondria.py index 027b4ed..a95712d 100644 --- a/synaptic_reconstruction/inference/mitochondria.py +++ b/synaptic_reconstruction/inference/mitochondria.py @@ -66,8 +66,7 @@ def segment_mitochondria( scale: Optional[List[float]] = None, mask: Optional[np.ndarray] = None, ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: - """ - Segment mitochondria in an input volume. + """Segment mitochondria in an input volume. Args: input_volume: The input volume to segment. diff --git a/synaptic_reconstruction/inference/util.py b/synaptic_reconstruction/inference/util.py index 5a799f3..3d17f99 100644 --- a/synaptic_reconstruction/inference/util.py +++ b/synaptic_reconstruction/inference/util.py @@ -4,17 +4,17 @@ from glob import glob from typing import Dict, Optional, Tuple -# Suppress annoying import warnings. -with warnings.catch_warnings(): - warnings.simplefilter("ignore") - import bioimageio.core +# # Suppress annoying import warnings. +# with warnings.catch_warnings(): +# warnings.simplefilter("ignore") +# import bioimageio.core import imageio.v3 as imageio import elf.parallel as parallel import numpy as np import torch import torch_em -import xarray +# import xarray from elf.io import open_file from scipy.ndimage import binary_closing @@ -80,9 +80,8 @@ def get_prediction( verbose: bool = True, with_channels: bool = False, mask: Optional[np.ndarray] = None, -): - """ - Run prediction on a given volume. +) -> np.ndarray: + """Run prediction on a given volume. This function will automatically choose the correct prediction implementation, depending on the model type. @@ -94,7 +93,8 @@ def get_prediction( tiling: The tiling configuration for the prediction. verbose: Whether to print timing information. with_channels: Whether to predict with channels. - mask: + mask: Optional binary mask. If given, the prediction will only be run in + the foreground region of the mask. Returns: The predicted volume. @@ -121,10 +121,9 @@ def get_prediction( # Run prediction with the bioimage.io library. if is_bioimageio: - # TODO determine if we use the old or new API and select the corresponding function if mask is not None: raise NotImplementedError - pred = get_prediction_bioimageio_old(input_volume, model_path, tiling, verbose) + raise NotImplementedError # Run prediction with the torch-em library. else: @@ -149,35 +148,6 @@ def get_prediction( return pred -def get_prediction_bioimageio_old( - input_volume: np.ndarray, # [z, y, x] - model_path: str, - tiling: Dict[str, Dict[str, int]], # {"tile": {"z": int, ...}, "halo": {"z": int, ...}} - verbose: bool = True, -): - """ - Run prediction using bioimage.io functionality on a given volume. - - Args: - input_volume: The input volume to predict on. - model_path: The path to the model checkpoint. - tiling: The tiling configuration for the prediction. - verbose: Whether to print timing information. - - Returns: - The predicted volume. - """ - # get foreground and boundary predictions from the model - t0 = time.time() - model = bioimageio.core.load_resource_description(model_path) - with bioimageio.core.create_prediction_pipeline(model) as pp: - input_ = xarray.DataArray(input_volume[None, None], dims=tuple("bczyx")) - pred = bioimageio.core.predict_with_tiling(pp, input_, tiling=tiling, verbose=verbose)[0].squeeze() - if verbose: - print("Prediction time in", time.time() - t0, "s") - return pred - - def get_prediction_torch_em( input_volume: np.ndarray, # [z, y, x] tiling: Dict[str, Dict[str, int]], # {"tile": {"z": int, ...}, "halo": {"z": int, ...}} @@ -187,8 +157,7 @@ def get_prediction_torch_em( with_channels: bool = False, mask: Optional[np.ndarray] = None, ) -> np.ndarray: - """ - Run prediction using torch-em on a given volume. + """Run prediction using torch-em on a given volume. Args: input_volume: The input volume to predict on. @@ -197,6 +166,8 @@ def get_prediction_torch_em( tiling: The tiling configuration for the prediction. verbose: Whether to print timing information. with_channels: Whether to predict with channels. + mask: Optional binary mask. If given, the prediction will only be run in + the foreground region of the mask. Returns: The predicted volume. @@ -292,9 +263,8 @@ def inference_helper( mask_input_ext: str = ".tif", force: bool = False, output_key: Optional[str] = None, -): - """ - Helper function to run segmentation for mrc files. +) -> None: + """Helper function to run segmentation for mrc files. Args: input_path: The path to the input data. @@ -378,8 +348,11 @@ def inference_helper( print(f"Saved segmentation to {output_path}.") -def get_default_tiling(): +def get_default_tiling() -> Dict[str, Dict[str, int]]: """Determine the tile shape and halo depending on the available VRAM. + + Returns: + The default tiling settings for the available computational resources. """ if torch.cuda.is_available(): print("Determining suitable tiling") @@ -415,16 +388,18 @@ def get_default_tiling(): return tiling -def parse_tiling(tile_shape, halo): - """ - Helper function to parse tiling parameter input from the command line. +def parse_tiling( + tile_shape: Tuple[int, int, int], + halo: Tuple[int, int, int] +) -> Dict[str, Dict[str, int]]: + """Helper function to parse tiling parameter input from the command line. Args: tile_shape: The tile shape. If None the default tile shape is used. halo: The halo. If None the default halo is used. Returns: - dict: the tiling specification + The tiling specification. """ default_tiling = get_default_tiling() diff --git a/synaptic_reconstruction/inference/vesicles.py b/synaptic_reconstruction/inference/vesicles.py index 237d95a..884138a 100644 --- a/synaptic_reconstruction/inference/vesicles.py +++ b/synaptic_reconstruction/inference/vesicles.py @@ -134,8 +134,7 @@ def segment_vesicles( exclude_boundary: bool = False, mask: Optional[np.ndarray] = None, ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: - """ - Segment vesicles in an input volume. + """Segment vesicles in an input volume or image. Args: input_volume: The input volume to segment. diff --git a/synaptic_reconstruction/morphology.py b/synaptic_reconstruction/morphology.py index 17acd2b..7fbce32 100644 --- a/synaptic_reconstruction/morphology.py +++ b/synaptic_reconstruction/morphology.py @@ -1,6 +1,7 @@ import multiprocessing as mp import warnings from concurrent import futures +from typing import Dict, List, Optional, Tuple import trimesh @@ -57,9 +58,30 @@ def dist(input_): def compute_radii( - vesicles, resolution, ids=None, derive_radius_from_distances=True, derive_distances_2d=True, min_size=500 -): + vesicles: np.ndarray, + resolution: Tuple[float, float, float], + ids: Optional[List[int]] = None, + derive_radius_from_distances: bool = True, + derive_distances_2d: bool = True, + min_size: int = 500, +) -> Tuple[List[int], Dict[int, float]]: """Compute the radii for a vesicle segmentation. + + Args: + vesicles: The vesicle segmentation. + resolution: The pixel / voxel size of the data. + ids: Vesicle ids to restrict the radius computation to. + derive_radius_from_distances: Whether to derive the radii + from the distance to the vesicle boundaries, or from the + axis fitted to the vesicle by scikit-image regionprops. + derive_distances_2d: Whether to derive the radii individually in 2d + or in 3d. Deriving the radii in 3d is beneficial for anisotropic + data or data that suffers from the missing wedge effect. + min_size: The minimal size for extracting the radii. + + Returns: + The ids of the extracted radii. + The radii that were computed. """ if derive_radius_from_distances: ids, radii = _compute_radii_distances( @@ -72,67 +94,69 @@ def compute_radii( return ids, radii -# TODO adjust the surface for open vs. closed structures -def compute_object_morphology(object_, structure_name, resolution=None): - """ - Compute the morphology (volume and surface area) of a 2D or 3D object. +def compute_object_morphology( + object_: np.ndarray, + structure_name: str, + resolution: Tuple[float, float, float] = None +) -> pd.DataFrame: + """Compute the volume and surface area of a 2D or 3D object. Args: - object_ (np.ndarray): 2D or 3D binary object array. - structure_name (str): Name of the structure being analyzed. - resolution (tuple): Physical spacing between nm. + object_: 2D or 3D binary object array. + structure_name: Name of the structure being analyzed. + resolution: The pixel / voxel size of the data. Returns: - pd.DataFrame: Morphology information containing volume and surface area. + Morphology information containing volume and surface area. """ if object_.ndim == 2: # Use find_contours for 2D data contours = find_contours(object_, level=0.5) - + # Compute perimeter (total length of all contours) perimeter = sum( np.sqrt(np.sum(np.diff(contour, axis=0)**2, axis=1)).sum() for contour in contours ) - + # Compute area (number of positive pixels) area = np.sum(object_ > 0) - + # Adjust for resolution if provided if resolution is not None: area *= resolution[0] * resolution[1] perimeter *= resolution[0] - + morphology = pd.DataFrame({ "structure": [structure_name], "area [pixel^2]" if resolution is None else "area [nm^2]": [area], "perimeter [pixel]" if resolution is None else "perimeter [nm]": [perimeter], }) - + elif object_.ndim == 3: # Use marching_cubes for 3D data verts, faces, normals, _ = marching_cubes( object_, spacing=(1.0, 1.0, 1.0) if resolution is None else resolution, ) - + mesh = trimesh.Trimesh(vertices=verts, faces=faces, vertex_normals=normals) surface = mesh.area if mesh.is_watertight: volume = np.abs(mesh.volume) else: - warnings.warn("Could not compute mesh volume; setting it to NaN.") + warnings.warn("Could not compute mesh surface for the volume; setting it to NaN.") volume = np.nan - + morphology = pd.DataFrame({ "structure": [structure_name], "volume [pixel^3]" if resolution is None else "volume [nm^3]": [volume], "surface [pixel^2]" if resolution is None else "surface [nm^2]": [surface], }) - + else: raise ValueError("Input object must be a 2D or 3D numpy array.") - + return morphology @@ -199,14 +223,6 @@ def _prune_skeleton_longest_path(skeleton): # Compute the longest path using MCP longest_path = _compute_longest_path(component, endpoints) - - # import napari - # v = napari.Viewer() - # v.add_labels(component) - # v.add_labels(longest_path) - # v.add_points(endpoints) - # napari.run() - pruned_skeleton |= longest_path return pruned_skeleton.astype(skeleton.dtype) @@ -217,12 +233,17 @@ def skeletonize_object( method: str = "skeletonize", prune: bool = True, min_prune_size: int = 10, -): +) -> np.ndarray: """Skeletonize a 3D object by inidividually skeletonizing each slice. Args: + segmentation: The segmented object. + method: The method to use for skeletonization. Either 'skeletonize' or 'medial_axis'. + prune: Whether to prune the skeleton. + min_prune_size: The minimal size of components after pruning. Returns: + The skeletonized object. """ assert method in ("skeletonize", "medial_axis") seg_thin = np.zeros_like(segmentation) diff --git a/synaptic_reconstruction/training/domain_adaptation.py b/synaptic_reconstruction/training/domain_adaptation.py index c4cb892..215d7fa 100644 --- a/synaptic_reconstruction/training/domain_adaptation.py +++ b/synaptic_reconstruction/training/domain_adaptation.py @@ -6,7 +6,7 @@ import torch_em.self_training as self_training from .semisupervised_training import get_unsupervised_loader -from .supervised_training import get_2d_model, get_3d_model, get_supervised_loader, determine_ndim +from .supervised_training import get_2d_model, get_3d_model, get_supervised_loader, _determine_ndim def mean_teacher_adaptation( @@ -28,12 +28,15 @@ def mean_teacher_adaptation( n_samples_train: Optional[int] = None, n_samples_val: Optional[int] = None, sampler: Optional[callable] = None, -): +) -> None: """Run domain adapation to transfer a network trained on a source domain for a supervised segmentation task to perform this task on a different target domain. We support different domain adaptation settings: - - + - unsupervised domain adaptation: the default mode when 'supervised_train_paths' and + 'supervised_val_paths' are not given. + - semi-supervised domain adaptation: domain adaptation on unlabeled and labeled data, + when 'supervised_train_paths' and 'supervised_val_paths' are given. Args: name: The name for the checkpoint to be trained. @@ -71,7 +74,7 @@ def mean_teacher_adaptation( based on the patch_shape and size of the volumes used for validation. """ assert (supervised_train_paths is None) == (supervised_val_paths is None) - is_2d, _ = determine_ndim(patch_shape) + is_2d, _ = _determine_ndim(patch_shape) if source_checkpoint is None: # training from scratch only makes sense if we have supervised training data diff --git a/synaptic_reconstruction/training/semisupervised_training.py b/synaptic_reconstruction/training/semisupervised_training.py index 8c2d0f2..1c9c0b8 100644 --- a/synaptic_reconstruction/training/semisupervised_training.py +++ b/synaptic_reconstruction/training/semisupervised_training.py @@ -6,7 +6,7 @@ import torch_em.self_training as self_training from torchvision import transforms -from .supervised_training import get_2d_model, get_3d_model, get_supervised_loader, determine_ndim +from .supervised_training import get_2d_model, get_3d_model, get_supervised_loader, _determine_ndim def weak_augmentations(p: float = 0.75) -> callable: @@ -61,7 +61,7 @@ def get_unsupervised_loader( else: roi = None - _, ndim = determine_ndim(patch_shape) + _, ndim = _determine_ndim(patch_shape) raw_transform = torch_em.transform.get_raw_transform() transform = torch_em.transform.get_augmentations(ndim=ndim) @@ -99,7 +99,7 @@ def semisupervised_training( n_samples_train: Optional[int] = None, n_samples_val: Optional[int] = None, check: bool = False, -): +) -> None: """Run semi-supervised segmentation training. Args: diff --git a/synaptic_reconstruction/training/supervised_training.py b/synaptic_reconstruction/training/supervised_training.py index 72a32f0..16cd1cb 100644 --- a/synaptic_reconstruction/training/supervised_training.py +++ b/synaptic_reconstruction/training/supervised_training.py @@ -19,6 +19,7 @@ def get_3d_model( initial_features: The number of features in the first level of the U-Net. The number of features increases by a factor of two in each level. final_activation: The activation applied to the last output layer. + Returns: The U-Net. """ @@ -60,14 +61,14 @@ def get_2d_model( return model -def adjust_patch_shape(data_shape, patch_shape): +def _adjust_patch_shape(data_shape, patch_shape): # If data is 2D and patch_shape is 3D, drop the extra dimension in patch_shape if data_shape == 2 and len(patch_shape) == 3: return patch_shape[1:] # Remove the leading dimension in patch_shape return patch_shape # Return the original patch_shape for 3D data -def determine_ndim(patch_shape): +def _determine_ndim(patch_shape): # Check for 2D or 3D training try: z, y, x = patch_shape @@ -120,7 +121,7 @@ def get_supervised_loader( Returns: The PyTorch dataloader. """ - _, ndim = determine_ndim(patch_shape) + _, ndim = _determine_ndim(patch_shape) if label_transform is not None: # A specific label transform was passed, do nothing. pass elif add_boundary_transform: @@ -137,7 +138,7 @@ def get_supervised_loader( label_transform = torch_em.transform.label.connected_components if ndim == 2: - adjusted_patch_shape = adjust_patch_shape(ndim, patch_shape) + adjusted_patch_shape = _adjust_patch_shape(ndim, patch_shape) transform = torch_em.transform.Compose( torch_em.transform.PadIfNecessary(adjusted_patch_shape), torch_em.transform.get_augmentations(2) ) @@ -239,7 +240,7 @@ def supervised_training( check_loader(val_loader, n_samples=4) return - is_2d, _ = determine_ndim(patch_shape) + is_2d, _ = _determine_ndim(patch_shape) if is_2d: model = get_2d_model(out_channels=out_channels) else: