diff --git a/helpers/visceral_make_splits.py b/helpers/visceral_make_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cbe3f56f0bf101555265ad22743dd64a1c45092
--- /dev/null
+++ b/helpers/visceral_make_splits.py
@@ -0,0 +1,118 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Generate visceral default JSON dataset for 3d binary classification tasks in mednet.
+
+Arguments of the scripts are as follow:
+ root-of-preprocessed-visceral-dataset
+ Full path to the root of the preprocessed visceral dataset.
+ Filenames in the resulting json are relative to this path.
+ See output format below.
+ output-folder
+ Full path to the folder where to output the default.json file containing the default split of data.
+ organ_1_id
+ Integer representing the ID of the first organ to include in the split dataset.
+ This organ will be labeled as 0 for the binary classification task.
+ For example, 237 corresponds to bladder.
+ organ_2_id
+ Integer representing the ID of the second organ to include in the split dataset.
+ This organ will be labeled as 1 for the binary classification task.
+ For example, 237 corresponds to bladder.
+
+Output format is the following:
+
+.. code:: json
+
+ {
+ "train": [
+ [
+ "<size>/<filename>",
+ # label is one of:
+ # 0: organ_1 / 1: organ_2
+ <label>,
+ ],
+ ...
+ ],
+ "validation": [
+ # same format as for train
+ ...
+ ]
+ "test": [
+ # same format as for train
+ ...
+ ]
+"""
+
+import json
+import os
+import pathlib
+import sys
+
+from sklearn.model_selection import train_test_split
+
+
+def split_files(
+ files: list[str],
+ train_size: float = 0.7,
+ test_size: float = 0.2,
+ validation_size: float = 0.1,
+):
+ train_files, temp_files = train_test_split(files, test_size=(1 - train_size))
+ test_files, validation_files = train_test_split(
+ temp_files, test_size=(validation_size / (test_size + validation_size))
+ )
+ return train_files, test_files, validation_files
+
+
+def save_to_json(
+ train_files: list[str],
+ test_files: list[str],
+ validation_files: list[str],
+ output_file: str,
+ organ_1_id: str,
+):
+ data = {
+ "train": [
+ [filename, 0 if organ_1_id in filename else 1] for filename in train_files
+ ],
+ "test": [
+ [filename, 0 if organ_1_id in filename else 1] for filename in test_files
+ ],
+ "validation": [
+ [filename, 0 if organ_1_id in filename else 1]
+ for filename in validation_files
+ ],
+ }
+
+ with pathlib.Path(output_file).open("w") as json_file:
+ json.dump(data, json_file, indent=2)
+
+
+def main():
+ if len(sys.argv) != 6:
+ print(__doc__)
+ print(
+ f"Usage: python3 {sys.argv[0]} <root-of-preprocessed-visceral-dataset> <output-folder> <organ_1_id> <organ_2_id> <size>"
+ )
+ sys.exit(0)
+
+ root_folder = sys.argv[1]
+ output_folder = sys.argv[2]
+ organ_1_id = sys.argv[3]
+ organ_2_id = sys.argv[4]
+ size = sys.argv[5]
+ output_file = pathlib.Path(output_folder) / "default.json"
+ input_folder = pathlib.Path(root_folder) / size
+ files = [
+ f"{size}/{file}"
+ for file in os.listdir(input_folder)
+ if organ_1_id in file or organ_2_id in file
+ ]
+ train_files, test_files, validation_files = split_files(files)
+
+ save_to_json(train_files, test_files, validation_files, output_file, organ_1_id)
+ print(f"Data saved to {output_file}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/helpers/visceral_preprocess.py b/helpers/visceral_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..3391f5128db47970f3948cfd558388e95566bbe2
--- /dev/null
+++ b/helpers/visceral_preprocess.py
@@ -0,0 +1,180 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Preprocess visceral dataset to prepare volume cubes for organ classification using 3D cnn.
+Example of use: 'python visceral_preprocess.py /idiap/temp/ojimenez/previous-projects/VISCERAL /idiap/home/ypannatier/visceral/preprocessed 237 16 '.
+
+Arguments of the scripts are as follow:
+ root-of-visceral-dataset
+ Full path to the root of the visceral dataset.
+ output_folder_path
+ Full path to the folder where the prepared cubes will be saved.
+ Note that the script will create a subfolder corresponding to the desired size of cube to avoid mixing volumes of different sizes.
+ organ_id
+ Integer representing the ID of the organ to process in the visceral dataset. For example, 237 corresponds to bladder.
+ size
+ Integer representing the size of the volume cube that will be output. Each volume will be of dimension SIZExSIZExSIZE.
+"""
+
+import os
+import pathlib
+import sys
+
+import torch
+import torchio as tio
+
+
+def get_mask_cube(mask_path: str, size: int) -> torch.Tensor:
+ """Create a mask of dimension SIZExSIZExSIZE from the input mask at the center of the non-zero area.
+
+ Parameters
+ ----------
+ mask_path
+ A string representing the full path to the mask file.
+ size
+ An integer representing the size of the cube.
+
+ Returns
+ -------
+ Tensor
+ The mask tensor representing a volume of dimension SIZExSIZExSIZE.
+ """
+ mask_image = tio.ScalarImage(mask_path)
+ mask_data = mask_image.data.bool().squeeze()
+ mask_image.unload()
+ ones_coords = torch.nonzero(mask_data)
+ center = torch.mean(ones_coords.float(), dim=0).long()
+ half_size = int(size) // 2
+ start_coords = center - half_size
+ end_coords = center + half_size
+ result = torch.zeros_like(mask_data)
+ slices = [
+ slice(max(0, start), min(end, dim))
+ for start, end, dim in zip(start_coords, end_coords, mask_data.shape)
+ ]
+ result[slices[0], slices[1], slices[2]] = 1
+ return result
+
+
+def get_masks(mask_paths: list[str], filters: list[str], volume_ids: list[str]):
+ """Find the list of usable masks corresponding to the desired organ.
+
+ Parameters
+ ----------
+ mask_paths
+ A list of strings representing the folders in which to search for masks.
+ filters
+ A list of strings corresponding to a substring of mask's file name. A valid mask must match one of the filters.
+ Each filter should contains the organ id.
+ Example: To get the white/black CT scans corresponding to an organ: "_1_CT_wb_{organ_id}".
+ volume_ids
+ A list containing the list of all patient ids retrieved from the volume dataset.
+ Valid masks must start by one of entry of volume ids.
+
+ Returns
+ -------
+ The list of valid masks as a list of strings. Each list contains 2 entries. First, the path to the mask folder, second the mask file name.
+ """
+ masks = []
+ for mask_path in mask_paths:
+ for mask_filter in filters:
+ available_masks = os.listdir(mask_path)
+ for mask in available_masks:
+ if (
+ mask.endswith(".nii.gz")
+ and mask_filter in mask
+ and mask.split("_")[0] in volume_ids
+ ):
+ masks.append([mask_path, mask])
+ return masks
+
+
+def main():
+ if len(sys.argv) != 5:
+ print(__doc__)
+ print(
+ f"Usage: python3 {sys.argv[0]} <root-of-visceral-dataset> <output_folder_path> <organ_id> <size>"
+ )
+ sys.exit(0)
+
+ root_path = sys.argv[1]
+ output_path = sys.argv[2]
+ organ_id = sys.argv[3]
+ size = sys.argv[4]
+
+ filters = [f"_1_CTce_ThAb_{organ_id}", f"_1_CT_wb_{organ_id}", f"_1_{organ_id}"]
+
+ annot_2_mask_path = (
+ pathlib.Path(root_path)
+ / "annotations"
+ / "Anatomy2"
+ / "anat2-trainingset"
+ / "Anat2_Segmentations"
+ )
+
+ annot_3_mask_path = (
+ pathlib.Path(root_path)
+ / "annotations"
+ / "Anatomy3"
+ / "Visceral-QC-testset"
+ / "qc-anat3-testset-segmentations"
+ )
+
+ silver_corpus_mask_path = (
+ pathlib.Path(root_path)
+ / "annotations"
+ / "SilverCorpus"
+ / "AnatomySilverCorpus"
+ / "BinarySegmentations"
+ )
+
+ mask_paths = [annot_2_mask_path, annot_3_mask_path, silver_corpus_mask_path]
+ volume_path = (
+ pathlib.Path(root_path)
+ / "volumes_for_annotation"
+ / "GeoS_oriented_Volumes(Annotators)"
+ )
+ output_size_path = pathlib.Path(output_path) / size
+
+ # Ensure required output folders exist
+ output_size_path.mkdir(parents=True, exist_ok=True)
+
+ volumes = os.listdir(volume_path)
+ volume_ids = [volume.split("_")[0] for volume in volumes]
+
+ masks = get_masks(mask_paths, filters, volume_ids)
+
+ print(f"Found {len(masks)} volumes to process...")
+ print("Generating volumes...")
+ for i, mask in enumerate(masks):
+ if i % 10 == 0:
+ print(f"Generated volumes: {i}/{len(masks)}")
+
+ patient_id = f'{mask[1].split("_")[0]}_1'
+ full_path = pathlib.Path(mask[0]) / mask[1]
+ volume_name = [volume for volume in volumes if patient_id in volume][0]
+ volume_full_path = pathlib.Path(volume_path) / volume_name
+ output_full_path = pathlib.Path(output_size_path) / mask[1]
+
+ if pathlib.Path.exists(output_full_path):
+ continue
+
+ try:
+ mask_cube = get_mask_cube(full_path, size)
+
+ volume_image = tio.ScalarImage(volume_full_path)
+ volume_data = volume_image.data.squeeze()
+ volume_image.unload()
+ volume_data = (
+ volume_data[mask_cube == 1]
+ .reshape(int(size), int(size), int(size))
+ .unsqueeze(0)
+ )
+ cropped_volume = tio.ScalarImage(tensor=volume_data)
+ cropped_volume.save(output_full_path)
+ except Exception as e:
+ print(f"Error: {e} while processing {full_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pixi.lock b/pixi.lock
index ec588a12e2e1d4cf8caaa0ade8459d5c5834d85a..b992a9e41a8834d01f8eb042c0a07369879372cd 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -65,10 +65,12 @@ environments:
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- conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.0-h59595ed_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/editables-0.5-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/eigen-3.4.0-h00ab1b0_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/entrypoints-0.4-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2.conda
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- conda: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-6.1.1-gpl_he44c6f3_112.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_hf1063bd_110.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.1-pyhd8ed1ab_0.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
@@ -112,6 +114,7 @@ environments:
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- conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.23.3-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/humanize-4.9.0-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
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- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda
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- conda: https://conda.anaconda.org/conda-forge/linux-64/libidn2-2.3.7-hd590300_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/libitk-5.3.0-h26001f5_8.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.10.2-hcae5a98_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-h01aab08_1018.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.3-pyhd8ed1ab_1.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/nh3-0.2.17-py312h4b3b743_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/nibabel-5.2.1-pyha770c72_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda
- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/shellingham-1.5.4-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/simpleitk-2.3.1-py312h30efb56_2.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/smmap-5.0.0-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.0-hdb0a2a9_1.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-w-1.0.0-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.12.5-pyha770c72_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/torchio-0.19.7-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/torchmetrics-1.4.0.post0-pyhd8ed1ab_0.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.4-pyhd8ed1ab_0.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/editables-0.5-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/osx-arm64/eigen-3.4.0-h1995070_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/entrypoints-0.4-pyhd8ed1ab_0.tar.bz2
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- conda: https://conda.anaconda.org/conda-forge/osx-arm64/expat-2.6.2-hebf3989_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ffmpeg-6.1.1-gpl_hc16618e_112.conda
+ - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fftw-3.3.10-nompi_h6637ab6_110.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.1-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fmt-10.2.1-h2ffa867_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
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- conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.23.3-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/humanize-4.9.0-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/hyperlink-21.0.0-pyhd3deb0d_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-73.2-hc8870d7_0.conda
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- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-devel-0.22.5-h8fbad5d_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libitk-5.3.0-h5b48add_8.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.0.0-hb547adb_1.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjxl-0.10.2-h07599a0_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libkml-1.3.0-h1eb4d9f_1018.conda
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- conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.3-pyhd8ed1ab_1.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/nh3-0.2.17-py312h5280bc4_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/nibabel-5.2.1-pyha770c72_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/nspr-4.35-hb7217d7_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/nss-3.100-hc6e9f88_0.conda
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py312h8442bc7_0.conda
@@ -679,6 +691,7 @@ environments:
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- conda: https://conda.anaconda.org/conda-forge/noarch/shellingham-1.5.4-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/osx-arm64/simpleitk-2.3.1-py312h650e478_2.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/smmap-5.0.0-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.0-hd04f947_1.conda
@@ -700,6 +713,7 @@ environments:
- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-w-1.0.0-pyhd8ed1ab_0.tar.bz2
- conda: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.12.5-pyha770c72_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/torchio-0.19.7-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/conda-forge/noarch/torchmetrics-1.4.0.post0-pyhd8ed1ab_0.conda
- conda: https://conda.anaconda.org/pytorch/osx-arm64/torchvision-0.17.2-py312_cpu.tar.bz2
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+ - libcxx >=16.0.6
+ - libexpat >=2.5.0,<3.0a0
+ - libjpeg-turbo >=3.0.0,<4.0a0
+ - libpng >=1.6.39,<1.7.0a0
+ - libtiff >=4.6.0,<4.7.0a0
+ - libzlib >=1.2.13,<2.0.0a0
+ - tbb >=2021.10.0
+ - zlib
+ license: Apache-2.0
+ license_family: APACHE
+ purls: []
+ size: 7110110
+ timestamp: 1701490808724
- kind: conda
name: libjpeg-turbo
version: 3.0.0
@@ -13618,7 +13868,7 @@ packages:
timestamp: 1704317789138
- kind: pypi
name: mednet
- version: 1.0.1.dev214+g1aab338.d20240613
+ version: 1.0.0b0
path: .
sha256: adff5bafa9edc506fbd485c396d2aefee9bfc29400b78e6d5950749ff33906ae
requires_dist:
@@ -14162,6 +14412,26 @@ packages:
- pkg:pypi/nh3?source=conda-forge-mapping
size: 582437
timestamp: 1711545995406
+- kind: conda
+ name: nibabel
+ version: 5.2.1
+ build: pyha770c72_0
+ subdir: noarch
+ noarch: python
+ url: https://conda.anaconda.org/conda-forge/noarch/nibabel-5.2.1-pyha770c72_0.conda
+ sha256: b5dd6d6ad4ba7c005567033c5ed6ecfbc34624c4d45fdd1882d5aeb9e5f04873
+ md5: 95ea4b21646fda1e2a34e0ddd895c9cc
+ depends:
+ - importlib_resources >=1.3
+ - numpy >=1.20
+ - packaging >=17
+ - python >=3.8
+ license: MIT
+ license_family: MIT
+ purls:
+ - pkg:pypi/nibabel?source=conda-forge-mapping
+ size: 2746114
+ timestamp: 1709044860369
- kind: conda
name: nodeenv
version: 1.9.1
@@ -17203,6 +17473,92 @@ packages:
- pkg:pypi/shellingham?source=conda-forge-mapping
size: 14568
timestamp: 1698144516278
+- kind: conda
+ name: simpleitk
+ version: 2.3.1
+ build: py311hb755f60_2
+ build_number: 2
+ subdir: linux-64
+ url: https://conda.anaconda.org/conda-forge/linux-64/simpleitk-2.3.1-py311hb755f60_2.conda
+ sha256: e0de95d1741616cd16d55366f4bb1914f03f0c752d87e5f1175622ff4615f977
+ md5: 7d9e0a06161dd3e2ac1645b795c40828
+ depends:
+ - libgcc-ng >=12
+ - libitk 5.3.0 h26001f5_8
+ - libstdcxx-ng >=12
+ - python >=3.11,<3.12.0a0
+ - python_abi 3.11.* *_cp311
+ license: Apache-2.0
+ license_family: APACHE
+ purls:
+ - pkg:pypi/simpleitk?source=conda-forge-mapping
+ size: 27193823
+ timestamp: 1701806692083
+- kind: conda
+ name: simpleitk
+ version: 2.3.1
+ build: py311hbaf5611_2
+ build_number: 2
+ subdir: osx-arm64
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/simpleitk-2.3.1-py311hbaf5611_2.conda
+ sha256: 171f109f9b4b1168fe507f537b540254f59676950082d272f19b2874156ce8ba
+ md5: 08d5361ed17fc065e5c09299a68fb9b7
+ depends:
+ - __osx >=10.9
+ - libcxx >=16.0.6
+ - libitk 5.3.0 h5b48add_8
+ - python >=3.11,<3.12.0a0
+ - python >=3.11,<3.12.0a0 *_cpython
+ - python_abi 3.11.* *_cp311
+ license: Apache-2.0
+ license_family: APACHE
+ purls:
+ - pkg:pypi/simpleitk?source=conda-forge-mapping
+ size: 19036652
+ timestamp: 1701806985204
+- kind: conda
+ name: simpleitk
+ version: 2.3.1
+ build: py312h30efb56_2
+ build_number: 2
+ subdir: linux-64
+ url: https://conda.anaconda.org/conda-forge/linux-64/simpleitk-2.3.1-py312h30efb56_2.conda
+ sha256: 451728343600bd5a32321276bee00270d88c9d95ca24cf10717d4c182a017250
+ md5: c438d05b72c796456fb18da5aec4670d
+ depends:
+ - libgcc-ng >=12
+ - libitk 5.3.0 h26001f5_8
+ - libstdcxx-ng >=12
+ - python >=3.12,<3.13.0a0
+ - python_abi 3.12.* *_cp312
+ license: Apache-2.0
+ license_family: APACHE
+ purls:
+ - pkg:pypi/simpleitk?source=conda-forge-mapping
+ size: 26877326
+ timestamp: 1701806680140
+- kind: conda
+ name: simpleitk
+ version: 2.3.1
+ build: py312h650e478_2
+ build_number: 2
+ subdir: osx-arm64
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/simpleitk-2.3.1-py312h650e478_2.conda
+ sha256: 60d2fa628b0badf8754a65d2dd7c62b75000a0b6dbec1c2e646b43f000842c99
+ md5: 3bc7cdff84074fa0844ae28ff84ec47f
+ depends:
+ - __osx >=10.9
+ - libcxx >=16.0.6
+ - libitk 5.3.0 h5b48add_8
+ - python >=3.12,<3.13.0a0
+ - python >=3.12,<3.13.0a0 *_cpython
+ - python_abi 3.12.* *_cp312
+ license: Apache-2.0
+ license_family: APACHE
+ purls:
+ - pkg:pypi/simpleitk?source=conda-forge-mapping
+ size: 18932800
+ timestamp: 1701807038993
- kind: conda
name: six
version: 1.16.0
@@ -18090,6 +18446,32 @@ packages:
- pkg:pypi/tomlkit?source=conda-forge-mapping
size: 37297
timestamp: 1715185504185
+- kind: conda
+ name: torchio
+ version: 0.19.7
+ build: pyhd8ed1ab_0
+ subdir: noarch
+ noarch: python
+ url: https://conda.anaconda.org/conda-forge/noarch/torchio-0.19.7-pyhd8ed1ab_0.conda
+ sha256: 9a5b75348350c0d976aa3752a9a1d712657010982df12401aef2b38424a54a0a
+ md5: d705b713c7e60f4729adca64ad457e4c
+ depends:
+ - click
+ - deprecated >=1.2.5
+ - humanize
+ - nibabel
+ - numpy
+ - python >=3.6,<3.13
+ - pytorch >=1.7
+ - scipy
+ - setuptools
+ - simpleitk !=2.0.*
+ - tqdm
+ license: Apache-2.0
+ purls:
+ - pkg:pypi/torchio?source=conda-forge-mapping
+ size: 120934
+ timestamp: 1718407590308
- kind: conda
name: torchmetrics
version: 1.4.0.post0
diff --git a/pyproject.toml b/pyproject.toml
index c5a5be4282f3acd6ab15c74fdc1d7dcc72869a40..b182482621e3cd4e3ef7356b1d3fe14227dc171e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -102,6 +102,7 @@ tensorboard = "*"
torchvision = { version = "~=0.17.2", channel = "pytorch" }
tqdm = "*"
versioningit = "*"
+torchio = ">=0.19.7,<0.20"
[tool.pixi.feature.self.pypi-dependencies]
mednet = { path = ".", editable = true }
@@ -245,6 +246,9 @@ alexnet-pretrained = "mednet.config.models.alexnet_pretrained"
densenet = "mednet.config.models.densenet"
densenet-pretrained = "mednet.config.models.densenet_pretrained"
+# 3D models
+cnn3d = "mednet.config.models.cnn3d"
+
# lists of data augmentations
affine = "mednet.config.augmentations.affine"
elastic = "mednet.config.augmentations.elastic"
@@ -409,6 +413,9 @@ nih-cxr14-padchest = "mednet.config.data.nih_cxr14_padchest.idiap"
# montgomery-shenzhen-indian-padchest aggregated dataset
montgomery-shenzhen-indian-padchest = "mednet.config.data.montgomery_shenzhen_indian_padchest.default"
+# VISCERAL dataset
+visceral = "mednet.config.data.visceral.default"
+
[tool.ruff]
line-length = 88
target-version = "py310"
diff --git a/src/mednet/config/data/visceral/__init__.py b/src/mednet/config/data/visceral/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/mednet/config/data/visceral/datamodule.py b/src/mednet/config/data/visceral/datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..023962cf76d8b98b948739aaf5ceedf31ad9d881
--- /dev/null
+++ b/src/mednet/config/data/visceral/datamodule.py
@@ -0,0 +1,117 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""VISCERAL dataset for 3D organ classification (only lungs and bladders).
+
+Loaded samples are not full scans but 16x16x16 volumes of organs.
+Database reference:
+"""
+
+import os
+import pathlib
+
+import torchio as tio
+
+from ....data.datamodule import CachingDataModule
+from ....data.split import make_split
+from ....data.typing import RawDataLoader as _BaseRawDataLoader
+from ....data.typing import Sample
+from ....utils.rc import load_rc
+
+CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
+"""Key to search for in the configuration file for the root directory of this
+database."""
+
+
+class RawDataLoader(_BaseRawDataLoader):
+ """A specialized raw-data-loader for the VISCERAL dataset."""
+
+ datadir: pathlib.Path
+ """This variable contains the base directory where the database raw data is
+ stored."""
+
+ def __init__(self) -> None:
+ self.datadir = pathlib.Path(
+ load_rc().get(
+ CONFIGURATION_KEY_DATADIR,
+ os.path.realpath(os.curdir),
+ ),
+ )
+
+ def sample(self, sample: tuple[str, int]) -> Sample:
+ """Load a single volume sample from the disk.
+
+ Parameters
+ ----------
+ sample
+ A tuple containing the path suffix, within the dataset root folder,
+ where to find the volume to be loaded and an integer, representing
+ the sample label.
+
+ Returns
+ -------
+ The sample representation.
+ """
+ clamp = tio.Clamp(out_min=-1000, out_max=2000)
+ rescale = tio.RescaleIntensity(percentiles=(0.5, 99.5))
+ preprocess = tio.Compose([clamp, rescale])
+ image = tio.ScalarImage(self.datadir / sample[0])
+ image = preprocess(image)
+ tensor = image.data
+ return tensor, dict(label=sample[1], name=sample[0])
+
+ def label(self, sample: tuple[str, int]) -> int:
+ """Load a single image sample label from the disk.
+
+ Parameters
+ ----------
+ sample
+ A tuple containing the path suffix, within the dataset root folder,
+ where to find the image to be loaded, and an integer, representing
+ the sample label.
+
+ Returns
+ -------
+ int
+ The integer label associated with the sample.
+ """
+
+ return sample[1]
+
+
+class DataModule(CachingDataModule):
+ """VISCERAL DataModule for 3D organ binary classification.
+
+ Data specifications:
+
+ * Raw data input (on disk):
+
+ * NIfTI volumes
+ * resolution: 16x16x16
+
+ * Output image:
+
+ * Transforms:
+
+ * Load raw NIfTI with :py:mod:`torchio`
+ * Clamp and Rescale intensity
+ * Convert to torch tensor
+
+ * Final specifications
+
+ * 32-bit floats, cubes 16x16x16 pixels
+ * Labels: 0 (bladder), 1 (lung)
+
+ Parameters
+ ----------
+ split_filename
+ Name of the .json file containing the split to load.
+ """
+
+ def __init__(self, split_filename: str):
+ super().__init__(
+ make_split(__package__, split_filename),
+ raw_data_loader=RawDataLoader(),
+ database_name=__package__.split(".")[-1],
+ split_name=pathlib.Path(split_filename).stem,
+ )
diff --git a/src/mednet/config/data/visceral/default.json b/src/mednet/config/data/visceral/default.json
new file mode 100644
index 0000000000000000000000000000000000000000..6bd8d5bdf56098c3037b4d545cae504cac96293c
--- /dev/null
+++ b/src/mednet/config/data/visceral/default.json
@@ -0,0 +1,353 @@
+{
+ "train": [
+ ["16/10000013_1_1302_117.nii.gz",1],
+ ["16/10000164_1_1302_117.nii.gz",1],
+ ["16/10000115_1_1302_117.nii.gz",1],
+ ["16/10000072_1_237_117.nii.gz",0],
+ ["16/10000148_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000022_1_CT_wb_1302_4.nii.gz",1],
+ ["16/10000163_1_237_117.nii.gz",0],
+ ["16/10000005_1_CT_wb_1302_7.nii.gz",1],
+ ["16/10000113_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000140_1_CTce_ThAb_237_5.nii.gz",0],
+ ["16/10000137_1_CTce_ThAb_237_7.nii.gz",0],
+ ["16/10000009_1_237_117.nii.gz",0],
+ ["16/10000090_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000115_1_237_117.nii.gz",0],
+ ["16/10000101_1_237_117.nii.gz",0],
+ ["16/10000168_1_1302_117.nii.gz",1],
+ ["16/10000054_1_1302_117.nii.gz",1],
+ ["16/10000059_1_237_117.nii.gz",0],
+ ["16/10000178_1_237_117.nii.gz",0],
+ ["16/10000053_1_237_117.nii.gz",0],
+ ["16/10000043_1_1302_117.nii.gz",1],
+ ["16/10000018_1_CT_wb_1302_4.nii.gz",1],
+ ["16/10000165_1_1302_117.nii.gz",1],
+ ["16/10000070_1_1302_117.nii.gz",1],
+ ["16/10000113_1_CTce_ThAb_237_6.nii.gz",0],
+ ["16/10000080_1_CT_wb_237_7.nii.gz",0],
+ ["16/10000145_1_CTce_ThAb_237_9.nii.gz",0],
+ ["16/10000140_1_CTce_ThAb_1302_5.nii.gz",1],
+ ["16/10000075_1_1302_117.nii.gz",1],
+ ["16/10000046_1_237_117.nii.gz",0],
+ ["16/10000108_1_CTce_ThAb_237_4.nii.gz",0],
+ ["16/10000109_1_CTce_ThAb_237_6.nii.gz",0],
+ ["16/10000055_1_1302_117.nii.gz",1],
+ ["16/10000004_1_1302_117.nii.gz",1],
+ ["16/10000150_1_237_117.nii.gz",0],
+ ["16/10000040_1_1302_117.nii.gz",1],
+ ["16/10000158_1_237_117.nii.gz",0],
+ ["16/10000183_1_1302_117.nii.gz",1],
+ ["16/10000025_1_CT_wb_237_8.nii.gz",0],
+ ["16/10000064_1_1302_117.nii.gz",1],
+ ["16/10000193_1_1302_117.nii.gz",1],
+ ["16/10000122_1_1302_117.nii.gz",1],
+ ["16/10000105_1_CTce_ThAb_237_4.nii.gz",0],
+ ["16/10000034_1_237_117.nii.gz",0],
+ ["16/10000112_1_CTce_ThAb_1302_8.nii.gz",1],
+ ["16/10000119_1_237_117.nii.gz",0],
+ ["16/10000138_1_CTce_ThAb_1302_8.nii.gz",1],
+ ["16/10000172_1_237_117.nii.gz",0],
+ ["16/10000072_1_1302_117.nii.gz",1],
+ ["16/10000012_1_1302_117.nii.gz",1],
+ ["16/10000078_1_1302_117.nii.gz",1],
+ ["16/10000096_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000125_1_1302_117.nii.gz",1],
+ ["16/10000191_1_1302_117.nii.gz",1],
+ ["16/10000055_1_237_117.nii.gz",0],
+ ["16/10000049_1_1302_117.nii.gz",1],
+ ["16/10000141_1_CTce_ThAb_237_5.nii.gz",0],
+ ["16/10000006_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000087_1_CT_wb_1302_4.nii.gz",1],
+ ["16/10000070_1_237_117.nii.gz",0],
+ ["16/10000080_1_CT_wb_1302_7.nii.gz",1],
+ ["16/10000015_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000011_1_CT_wb_237_7.nii.gz",0],
+ ["16/10000159_1_1302_117.nii.gz",1],
+ ["16/10000077_1_237_117.nii.gz",0],
+ ["16/10000087_1_CT_wb_237_4.nii.gz",0],
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+ ["16/10000153_1_1302_117.nii.gz",1],
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+ ["16/10000044_1_237_117.nii.gz",0],
+ ["16/10000175_1_237_117.nii.gz",0],
+ ["16/10000143_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000160_1_1302_117.nii.gz",1],
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+ ["16/10000203_1_1302_117.nii.gz",1],
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+ ["16/10000157_1_1302_117.nii.gz",1],
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+ ["16/10000111_1_CTce_ThAb_237_6.nii.gz",0],
+ ["16/10000050_1_1302_117.nii.gz",1],
+ ["16/10000128_1_CTce_ThAb_1302_5.nii.gz",1],
+ ["16/10000134_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000119_1_1302_117.nii.gz",1],
+ ["16/10000092_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000205_1_1302_117.nii.gz",1],
+ ["16/10000169_1_237_117.nii.gz",0],
+ ["16/10000155_1_1302_117.nii.gz",1],
+ ["16/10000044_1_1302_117.nii.gz",1],
+ ["16/10000134_1_CTce_ThAb_237_6.nii.gz",0],
+ ["16/10000016_1_CT_wb_1302_9.nii.gz",1],
+ ["16/10000031_1_237_117.nii.gz",0],
+ ["16/10000020_1_CT_wb_237_8.nii.gz",0],
+ ["16/10000122_1_237_117.nii.gz",0],
+ ["16/10000123_1_1302_117.nii.gz",1],
+ ["16/10000062_1_237_117.nii.gz",0],
+ ["16/10000042_1_1302_117.nii.gz",1],
+ ["16/10000135_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000106_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000051_1_1302_117.nii.gz",1],
+ ["16/10000198_1_237_117.nii.gz",0],
+ ["16/10000136_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000045_1_237_117.nii.gz",0],
+ ["16/10000147_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000086_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000131_1_CTce_ThAb_1302_4.nii.gz",1],
+ ["16/10000174_1_237_117.nii.gz",0],
+ ["16/10000170_1_237_117.nii.gz",0],
+ ["16/10000049_1_237_117.nii.gz",0],
+ ["16/10000116_1_237_117.nii.gz",0],
+ ["16/10000137_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000111_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000180_1_1302_117.nii.gz",1],
+ ["16/10000075_1_237_117.nii.gz",0],
+ ["16/10000161_1_237_117.nii.gz",0],
+ ["16/10000011_1_CT_wb_1302_7.nii.gz",1],
+ ["16/10000094_1_CT_wb_1302_7.nii.gz",1],
+ ["16/10000015_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000145_1_CTce_ThAb_1302_7.nii.gz",1],
+ ["16/10000085_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000033_1_237_117.nii.gz",0],
+ ["16/10000097_1_1302_117.nii.gz",1],
+ ["16/10000141_1_CTce_ThAb_1302_5.nii.gz",1],
+ ["16/10000130_1_CTce_ThAb_237_5.nii.gz",0],
+ ["16/10000171_1_237_117.nii.gz",0],
+ ["16/10000163_1_1302_117.nii.gz",1],
+ ["16/10000021_1_CT_wb_1302_9.nii.gz",1],
+ ["16/10000184_1_237_117.nii.gz",0],
+ ["16/10000114_1_237_117.nii.gz",0],
+ ["16/10000160_1_237_117.nii.gz",0],
+ ["16/10000071_1_237_117.nii.gz",0],
+ ["16/10000084_1_1302_117.nii.gz",1],
+ ["16/10000135_1_CTce_ThAb_237_7.nii.gz",0],
+ ["16/10000117_1_237_117.nii.gz",0],
+ ["16/10000149_1_CTce_ThAb_1302_8.nii.gz",1],
+ ["16/10000162_1_237_117.nii.gz",0],
+ ["16/10000025_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000100_1_CTce_ThAb_237_6.nii.gz",0],
+ ["16/10000200_1_1302_117.nii.gz",1],
+ ["16/10000096_1_CT_wb_237_9.nii.gz",0],
+ ["16/10000024_1_237_117.nii.gz",0],
+ ["16/10000118_1_1302_117.nii.gz",1],
+ ["16/10000112_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000159_1_237_117.nii.gz",0],
+ ["16/10000196_1_1302_117.nii.gz",1],
+ ["16/10000204_1_237_117.nii.gz",0],
+ ["16/10000019_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000047_1_237_117.nii.gz",0],
+ ["16/10000035_1_237_117.nii.gz",0],
+ ["16/10000012_1_237_117.nii.gz",0],
+ ["16/10000128_1_CTce_ThAb_237_5.nii.gz",0],
+ ["16/10000174_1_1302_117.nii.gz",1],
+ ["16/10000013_1_237_117.nii.gz",0],
+ ["16/10000056_1_1302_117.nii.gz",1],
+ ["16/10000020_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000076_1_1302_117.nii.gz",1],
+ ["16/10000177_1_237_117.nii.gz",0],
+ ["16/10000198_1_1302_117.nii.gz",1],
+ ["16/10000078_1_237_117.nii.gz",0],
+ ["16/10000125_1_237_117.nii.gz",0],
+ ["16/10000082_1_CT_wb_1302_6.nii.gz",1],
+ ["16/10000091_1_237_117.nii.gz",0],
+ ["16/10000085_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000089_1_CT_wb_1302_8.nii.gz",1],
+ ["16/10000110_1_CTce_ThAb_1302_9.nii.gz",1],
+ ["16/10000053_1_1302_117.nii.gz",1]
+ ],
+ "validation": [
+ ["16/10000203_1_237_117.nii.gz",0],
+ ["16/10000022_1_CT_wb_237_4.nii.gz",0],
+ ["16/10000017_1_CT_wb_237_9.nii.gz",0],
+ ["16/10000166_1_1302_117.nii.gz",1],
+ ["16/10000016_1_CT_wb_237_9.nii.gz",0],
+ ["16/10000057_1_1302_117.nii.gz",1],
+ ["16/10000165_1_237_117.nii.gz",0],
+ ["16/10000021_1_CT_wb_237_9.nii.gz",0],
+ ["16/10000098_1_237_117.nii.gz",0],
+ ["16/10000068_1_237_117.nii.gz",0],
+ ["16/10000095_1_CT_wb_1302_7.nii.gz",1],
+ ["16/10000067_1_CT_wb_237_9.nii.gz",0],
+ ["16/10000069_1_1302_117.nii.gz",1],
+ ["16/10000201_1_1302_117.nii.gz",1],
+ ["16/10000132_1_CTce_ThAb_1302_8.nii.gz",1],
+ ["16/10000181_1_1302_117.nii.gz",1],
+ ["16/10000101_1_1302_117.nii.gz",1],
+ ["16/10000067_1_CT_wb_1302_9.nii.gz",1],
+ ["16/10000027_1_237_117.nii.gz",0],
+ ["16/10000187_1_237_117.nii.gz",0],
+ ["16/10000065_1_237_117.nii.gz",0],
+ ["16/10000186_1_237_117.nii.gz",0],
+ ["16/10000186_1_1302_117.nii.gz",1],
+ ["16/10000185_1_1302_117.nii.gz",1],
+ ["16/10000097_1_237_117.nii.gz",0],
+ ["16/10000023_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000098_1_1302_117.nii.gz",1],
+ ["16/10000099_1_CT_wb_237_5.nii.gz",0],
+ ["16/10000073_1_1302_117.nii.gz",1],
+ ["16/10000184_1_1302_117.nii.gz",1],
+ ["16/10000093_1_1302_117.nii.gz",1],
+ ["16/10000086_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000007_1_237_117.nii.gz",0],
+ ["16/10000038_1_1302_117.nii.gz",1],
+ ["16/10000050_1_237_117.nii.gz",0],
+ ["16/10000093_1_237_117.nii.gz",0],
+ ["16/10000183_1_237_117.nii.gz",0],
+ ["16/10000060_1_237_117.nii.gz",0],
+ ["16/10000089_1_CT_wb_237_8.nii.gz",0],
+ ["16/10000124_1_237_117.nii.gz",0],
+ ["16/10000152_1_237_117.nii.gz",0],
+ ["16/10000158_1_1302_117.nii.gz",1],
+ ["16/10000193_1_237_117.nii.gz",0],
+ ["16/10000167_1_237_117.nii.gz",0],
+ ["16/10000008_1_237_117.nii.gz",0],
+ ["16/10000071_1_1302_117.nii.gz",1],
+ ["16/10000171_1_1302_117.nii.gz",1],
+ ["16/10000120_1_237_117.nii.gz",0],
+ ["16/10000100_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000154_1_237_117.nii.gz",0],
+ ["16/10000082_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000120_1_1302_117.nii.gz",1],
+ ["16/10000175_1_1302_117.nii.gz",1],
+ ["16/10000026_1_237_117.nii.gz",0],
+ ["16/10000178_1_1302_117.nii.gz",1],
+ ["16/10000192_1_1302_117.nii.gz",1],
+ ["16/10000155_1_237_117.nii.gz",0],
+ ["16/10000152_1_1302_117.nii.gz",1],
+ ["16/10000005_1_CT_wb_237_7.nii.gz",0],
+ ["16/10000191_1_237_117.nii.gz",0],
+ ["16/10000073_1_237_117.nii.gz",0],
+ ["16/10000181_1_237_117.nii.gz",0],
+ ["16/10000014_1_CT_wb_1302_5.nii.gz",1],
+ ["16/10000017_1_CT_wb_1302_9.nii.gz",1],
+ ["16/10000081_1_CT_wb_237_6.nii.gz",0],
+ ["16/10000079_1_CT_wb_237_7.nii.gz",0],
+ ["16/10000153_1_237_117.nii.gz",0],
+ ["16/10000164_1_237_117.nii.gz",0],
+ ["16/10000167_1_1302_117.nii.gz",1]
+ ],
+ "test": [
+ ["16/10000109_1_CTce_ThAb_1302_6.nii.gz",1],
+ ["16/10000039_1_237_117.nii.gz",0],
+ ["16/10000118_1_237_117.nii.gz",0],
+ ["16/10000190_1_1302_117.nii.gz",1],
+ ["16/10000110_1_CTce_ThAb_237_9.nii.gz",0],
+ ["16/10000180_1_237_117.nii.gz",0],
+ ["16/10000131_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000121_1_1302_117.nii.gz",1],
+ ["16/10000127_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000173_1_237_117.nii.gz",0],
+ ["16/10000166_1_237_117.nii.gz",0],
+ ["16/10000105_1_CTce_ThAb_1302_4.nii.gz",1],
+ ["16/10000177_1_1302_117.nii.gz",1],
+ ["16/10000031_1_1302_117.nii.gz",1],
+ ["16/10000188_1_1302_117.nii.gz",1],
+ ["16/10000069_1_237_117.nii.gz",0],
+ ["16/10000143_1_CTce_ThAb_237_7.nii.gz",0],
+ ["16/10000014_1_CT_wb_237_5.nii.gz",0],
+ ["16/10000133_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000199_1_1302_117.nii.gz",1],
+ ["16/10000205_1_237_117.nii.gz",0],
+ ["16/10000121_1_237_117.nii.gz",0],
+ ["16/10000149_1_CTce_ThAb_237_8.nii.gz",0],
+ ["16/10000199_1_237_117.nii.gz",0],
+ ["16/10000032_1_237_117.nii.gz",0],
+ ["16/10000156_1_237_117.nii.gz",0],
+ ["16/10000092_1_CT_wb_237_4.nii.gz",0],
+ ["16/10000037_1_1302_117.nii.gz",1],
+ ["16/10000172_1_1302_117.nii.gz",1],
+ ["16/10000043_1_237_117.nii.gz",0],
+ ["16/10000204_1_1302_117.nii.gz",1],
+ ["16/10000185_1_237_117.nii.gz",0],
+ ["16/10000108_1_CTce_ThAb_1302_4.nii.gz",1],
+ ["16/10000194_1_237_117.nii.gz",0],
+ ["16/10000059_1_1302_117.nii.gz",1]
+ ]
+}
diff --git a/src/mednet/config/data/visceral/default.py b/src/mednet/config/data/visceral/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..46942bb7d2015a9223a9069d7f1d166af3a4c5c7
--- /dev/null
+++ b/src/mednet/config/data/visceral/default.py
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""VISCERAL dataset for 3D organ classification.
+
+Database reference:
+
+See :py:class:`mednet.config.data.visceral.datamodule.DataModule` for
+technical details.
+"""
+
+from mednet.config.data.visceral.datamodule import DataModule
+
+datamodule = DataModule("default.json")
diff --git a/src/mednet/config/models/cnn3d.py b/src/mednet/config/models/cnn3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..305b545287077a6b67c54da4fcca25025a2c6615
--- /dev/null
+++ b/src/mednet/config/models/cnn3d.py
@@ -0,0 +1,15 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Simple CNN for 3D organ classification, to be trained from scratch."""
+
+from torch.nn import BCEWithLogitsLoss
+from torch.optim import Adam
+
+from mednet.models.cnn3d import Conv3DNet
+
+model = Conv3DNet(
+ loss_type=BCEWithLogitsLoss,
+ optimizer_type=Adam,
+ optimizer_arguments=dict(lr=8e-5),
+)
diff --git a/src/mednet/models/cnn3d.py b/src/mednet/models/cnn3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0be0e62e6c66431e0d14ac1d2ecddcfd91cf1d7
--- /dev/null
+++ b/src/mednet/models/cnn3d.py
@@ -0,0 +1,206 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import logging
+import typing
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F # noqa: N812
+import torch.optim.optimizer
+import torch.utils.data
+
+from ..data.typing import TransformSequence
+from .model import Model
+from .separate import separate
+
+logger = logging.getLogger(__name__)
+
+
+class Conv3DNet(Model):
+ """Implementation of 3D CNN.
+
+ This network has a linear output. You should use losses with ``WithLogit``
+ instead of cross-entropy versions when training.
+
+ Parameters
+ ----------
+ loss_type
+ The loss to be used for training and evaluation.
+
+ .. warning::
+
+ The loss should be set to always return batch averages (as opposed
+ to the batch sum), as our logging system expects it so.
+ loss_arguments
+ Arguments to the loss.
+ optimizer_type
+ The type of optimizer to use for training.
+ optimizer_arguments
+ Arguments to the optimizer after ``params``.
+ augmentation_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
+ num_classes
+ Number of outputs (classes) for this model.
+ """
+
+ def __init__(
+ self,
+ loss_type: torch.nn.Module = torch.nn.BCEWithLogitsLoss,
+ loss_arguments: dict[str, typing.Any] = {},
+ optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
+ optimizer_arguments: dict[str, typing.Any] = {},
+ augmentation_transforms: TransformSequence = [],
+ num_classes: int = 1,
+ ):
+ super().__init__(
+ loss_type,
+ loss_arguments,
+ optimizer_type,
+ optimizer_arguments,
+ augmentation_transforms,
+ num_classes,
+ )
+
+ self.name = "cnn3D"
+ self.num_classes = num_classes
+
+ self.model_transforms = []
+
+ # First convolution block
+ self.conv3d_1_1 = nn.Conv3d(
+ in_channels=1, out_channels=4, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_1_2 = nn.Conv3d(
+ in_channels=4, out_channels=16, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_1_3 = nn.Conv3d(
+ in_channels=1, out_channels=16, kernel_size=1, stride=1
+ )
+ self.batch_norm_1_1 = nn.BatchNorm3d(4)
+ self.batch_norm_1_2 = nn.BatchNorm3d(16)
+ self.batch_norm_1_3 = nn.BatchNorm3d(16)
+
+ # Second convolution block
+ self.conv3d_2_1 = nn.Conv3d(
+ in_channels=16, out_channels=24, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_2_2 = nn.Conv3d(
+ in_channels=24, out_channels=32, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_2_3 = nn.Conv3d(
+ in_channels=16, out_channels=32, kernel_size=1, stride=1
+ )
+ self.batch_norm_2_1 = nn.BatchNorm3d(24)
+ self.batch_norm_2_2 = nn.BatchNorm3d(32)
+ self.batch_norm_2_3 = nn.BatchNorm3d(32)
+
+ # Third convolution block
+ self.conv3d_3_1 = nn.Conv3d(
+ in_channels=32, out_channels=40, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_3_2 = nn.Conv3d(
+ in_channels=40, out_channels=48, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_3_3 = nn.Conv3d(
+ in_channels=32, out_channels=48, kernel_size=1, stride=1
+ )
+ self.batch_norm_3_1 = nn.BatchNorm3d(40)
+ self.batch_norm_3_2 = nn.BatchNorm3d(48)
+ self.batch_norm_3_3 = nn.BatchNorm3d(48)
+
+ # Fourth convolution block
+ self.conv3d_4_1 = nn.Conv3d(
+ in_channels=48, out_channels=56, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_4_2 = nn.Conv3d(
+ in_channels=56, out_channels=64, kernel_size=3, stride=1, padding=1
+ )
+ self.conv3d_4_3 = nn.Conv3d(
+ in_channels=48, out_channels=64, kernel_size=1, stride=1
+ )
+ self.batch_norm_4_1 = nn.BatchNorm3d(56)
+ self.batch_norm_4_2 = nn.BatchNorm3d(64)
+ self.batch_norm_4_3 = nn.BatchNorm3d(64)
+
+ self.pool = nn.MaxPool3d(2)
+ self.global_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
+ self.dropout = nn.Dropout(0.3)
+ self.fc1 = nn.Linear(64, 32)
+ self.fc2 = nn.Linear(32, num_classes)
+
+ def forward(self, x):
+ x = self.normalizer(x) # type: ignore
+
+ # First convolution block
+ _x = x
+ x = F.relu(self.batch_norm_1_1(self.conv3d_1_1(x)))
+ x = F.relu(self.batch_norm_1_2(self.conv3d_1_2(x)))
+ x = (x + F.relu(self.batch_norm_1_3(self.conv3d_1_3(_x)))) / 2
+ x = self.pool(x)
+
+ # Second convolution block
+
+ _x = x
+ x = F.relu(self.batch_norm_2_1(self.conv3d_2_1(x)))
+ x = F.relu(self.batch_norm_2_2(self.conv3d_2_2(x)))
+ x = (x + F.relu(self.batch_norm_2_3(self.conv3d_2_3(_x)))) / 2
+ x = self.pool(x)
+
+ # Third convolution block
+
+ _x = x
+ x = F.relu(self.batch_norm_3_1(self.conv3d_3_1(x)))
+ x = F.relu(self.batch_norm_3_2(self.conv3d_3_2(x)))
+ x = (x + F.relu(self.batch_norm_3_3(self.conv3d_3_3(_x)))) / 2
+ x = self.pool(x)
+
+ # Fourth convolution block
+
+ _x = x
+ x = F.relu(self.batch_norm_4_1(self.conv3d_4_1(x)))
+ x = F.relu(self.batch_norm_4_2(self.conv3d_4_2(x)))
+ x = (x + F.relu(self.batch_norm_4_3(self.conv3d_4_3(_x)))) / 2
+
+ x = self.global_pool(x)
+ x = x.view(x.size(0), x.size(1))
+
+ x = F.relu(self.fc1(x))
+ x = self.dropout(x)
+ return self.fc2(x)
+
+ # x = F.log_softmax(x, dim=1) # 0 is batch size
+
+ def training_step(self, batch, _):
+ images = batch[0]
+ labels = batch[1]["label"]
+
+ # Increase label dimension if too low
+ # Allows single and multiclass usage
+ if labels.ndim == 1:
+ labels = torch.reshape(labels, (labels.shape[0], 1))
+
+ # Forward pass on the network
+ outputs = self(self.augmentation_transforms(images))
+
+ return self._train_loss(outputs, labels.float())
+
+ def validation_step(self, batch, batch_idx, dataloader_idx=0):
+ images = batch[0]
+ labels = batch[1]["label"]
+
+ # Increase label dimension if too low
+ # Allows single and multiclass usage
+ if labels.ndim == 1:
+ labels = torch.reshape(labels, (labels.shape[0], 1))
+
+ # data forwarding on the existing network
+ outputs = self(images)
+ return self._validation_loss(outputs, labels.float())
+
+ def predict_step(self, batch, batch_idx, dataloader_idx=0):
+ outputs = self(batch[0])
+ probabilities = torch.sigmoid(outputs)
+ return separate((probabilities, batch[1]))
diff --git a/tests/test_visceral.py b/tests/test_visceral.py
new file mode 100644
index 0000000000000000000000000000000000000000..9743577e1dc68f819c556177dc2e77dfa616baeb
--- /dev/null
+++ b/tests/test_visceral.py
@@ -0,0 +1,46 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Tests for VISCERAL dataset."""
+
+import pytest
+from click.testing import CliRunner
+
+
+def id_function(val):
+ if isinstance(val, dict):
+ return str(val)
+ return repr(val)
+
+
+@pytest.mark.parametrize(
+ "split,lenghts",
+ [
+ ("default", dict(train=241, validation=69, test=35)),
+ ],
+ ids=id_function, # just changes how pytest prints it
+)
+def test_protocol_consistency(
+ database_checkers,
+ split: str,
+ lenghts: dict[str, int],
+):
+ from mednet.data.split import make_split
+
+ database_checkers.check_split(
+ make_split("mednet.config.data.visceral", f"{split}.json"),
+ lengths=lenghts,
+ prefixes=("16/10000"),
+ possible_labels=(0, 1),
+ )
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.visceral")
+def test_database_check():
+ from mednet.scripts.database import check
+
+ runner = CliRunner()
+ result = runner.invoke(check, ["visceral"])
+ assert (
+ result.exit_code == 0
+ ), f"Exit code {result.exit_code} != 0 -- Output:\n{result.output}"