✨Fully support decathlon datalist

.github/workflows/deploy_and_test.yml

@@ -37,11 +37,6 @@ jobs:
           os: windows-latest,
           python-version: "3.9"
           }
-        - {
-          name: "ubuntu-latest - Python 3.8",
-          os: ubuntu-latest,
-          python-version: "3.8"
-          }
         - {
           name: "ubuntu-latest - Python 3.9",
           os: ubuntu-latest,

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -26,34 +26,34 @@ repos:
       - id: requirements-txt-fixer
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.277
+    rev: v0.3.2
     hooks:
       - id: ruff
         name: ruff
         args: [ --fix, --exit-non-zero-on-fix ]
 
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 24.2.0
     hooks:
       - id: black
         name: format code
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
         name: format imports
         args: ["--profile", "black"]
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.15.1
     hooks:
       - id: pyupgrade
         name: upgrade code
-        args: ["--py38-plus"]
+        args: ["--py39-plus"]
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.16
+    rev: 0.7.17
     hooks:
       - id: mdformat
         name: format markdown
@@ -63,25 +63,30 @@ repos:
         exclude: CHANGELOG.md
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         name: check PEP8
         args: ["--ignore=E501,W503,E203"]
 
   - repo: https://github.com/hadialqattan/pycln
-    rev: v2.1.5
+    rev: v2.4.0
     hooks:
       - id: pycln
         name: prune imports
         args: [--expand-stars]
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.7.0
+    rev: 1.8.4
     hooks:
       - id: nbqa-black
         additional_dependencies: [black]
         name: format notebooks
       - id: nbqa-mypy
         additional_dependencies: [mypy]
         name: static analysis for notebooks
+
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.19.0
+    hooks:
+      - id: typos

README.md

@@ -80,7 +80,7 @@ segmantic-unet train-config -c config.yml
 
 ## What is this tisse_list?
 
-The example above included a tissue_list option. This is a path to a text file specifying the labels contained in a segmented image. By convention the 'label=0' is the background and is ommited from the the format. A segmentation with three tissues 'Bone'=1, 'Fat'=2, and 'Skin'=3 would be specified as follows:
+The example above included a tissue_list option. This is a path to a text file specifying the labels contained in a segmented image. By convention the 'label=0' is the background and is omitted from the the format. A segmentation with three tissues 'Bone'=1, 'Fat'=2, and 'Skin'=3 would be specified as follows:
 
 ```
     V7
@@ -96,7 +96,7 @@ Instead of providing the 'image_dir'/'labels_dir' pair, the training data can al
 
 ```json
 {
-    "dataset": ["/dataA/dataset.json", "/dataB/dataset.json"],
+    "datalist": ["/dataA/dataset.json", "/dataB/dataset.json"],
     "output_dir": "<path where trained model and logs are saved>",
     "Etc": "etc"
 }

pyproject.toml

@@ -6,13 +6,12 @@ build-backend = "setuptools.build_meta"
 name = "segmantic"
 authors = [{name = "Bryn Lloyd", email = "[email protected]"}]
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license = {file = "LICENSE"}
 dynamic = ["version"]
 classifiers = [
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
@@ -72,7 +71,7 @@ profile = "black"
 disallow_untyped_defs = false
 warn_unused_configs = true
 warn_redundant_casts = true
-warn_unused_ignores = true
+warn_unused_ignores = false
 warn_return_any = true
 strict_equality = true
 no_implicit_optional = false
@@ -90,9 +89,15 @@ module = [
 disallow_untyped_defs = true
 
 [[tool.mypy.overrides]]
-module = "itk.*,PIL,matplotlib.*,torch,torchvision.*,numba,setuptools,pytest,typer.*,click,colorama,nibabel,sklearn.*,yaml,scipy.*,adabelief_pytorch,h5py,SimpleITK,vtk.*,sitk_cli"
+module = "itk.*,PIL,matplotlib.*,torchvision.*,numba,setuptools,pytest,typer.*,click,colorama,nibabel,sklearn.*,yaml,scipy.*,adabelief_pytorch,h5py,SimpleITK,vtk.*,sitk_cli"
 ignore_missing_imports = true
 
 [tool.ruff]
-select = ["E", "F"]
-ignore = ["E501"]
+lint.select = ["E", "F"]
+lint.ignore = ["E501"]
+
+[tool.typos]
+
+[tool.typos.default.extend-identifiers]
+# *sigh* monai adds 'd' to dictionary transforms
+SpatialPadd = "SpatialPadd"

scripts/check_masks.py

@@ -29,5 +29,18 @@ def fix_binary_masks(directory: Path, file_glob: str = "*.nii.gz"):
             )
 
 
+def round_half_up(input_dir: Path, output_dir: Path = None):
+    import SimpleITK as sitk
+
+    for f in input_dir.glob("*.nii.gz"):
+        img = sitk.ReadImage(f)
+        img_np = sitk.GetArrayViewFromImage(img)
+        imin, imax = np.min(img_np), np.max(img_np)
+        if imin < 0 or imax > 3:
+            print(f"{f.name}: [{imin}, {imax}]")
+        if img.GetPixelID() in (sitk.sitkFloat32, sitk.sitkFloat64):
+            print(f"{f.name}: {img.GetPixelIDTypeAsString()}")
+
+
 if __name__ == "__main__":
-    typer.run(fix_binary_masks)
+    typer.run(round_half_up)

scripts/check_training_data.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+
+import numpy as np
+import SimpleITK as sitk
+import typer
+
+from segmantic.utils.file_iterators import find_matching_files
+
+
+def check_training_data(
+    image_dir: Path, labels_dir: Path, copy_image_information: bool = False
+):
+    matches = find_matching_files([image_dir / "*.nii.gz", labels_dir / "*.nii.gz"])
+    for p in matches:
+        img = sitk.ReadImage(p[0])
+        lbl = sitk.ReadImage(p[1])
+        if img.GetSize() != lbl.GetSize():
+            print(f"Size mismatch {p[0].name}: {img.GetSize()} != {lbl.GetSize()}")
+            continue
+        if copy_image_information:
+            lbl.CopyInformation(img)
+            sitk.WriteImage(sitk.Cast(lbl, sitk.sitkUInt8), p[1])
+        elif img.GetSpacing() != lbl.GetSpacing() or img.GetOrigin() != lbl.GetOrigin():
+            np.testing.assert_almost_equal(
+                img.GetSpacing(), lbl.GetSpacing(), decimal=2
+            )
+            np.testing.assert_almost_equal(img.GetOrigin(), lbl.GetOrigin(), decimal=2)
+
+
+if __name__ == "__main__":
+    typer.run(check_training_data)

scripts/evaluate_segmentations.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 from pathlib import Path
 
 import SimpleITK as sitk

scripts/extract_unet.py

@@ -0,0 +1,22 @@
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+
+def extract_unet(input_file: Path, output_file: Optional[Path] = None):
+    """Load segmantic unet lightning module and export inner monai UNet"""
+    import torch
+
+    from segmantic.seg.monai_unet import Net
+
+    if output_file is None:
+        output_file = input_file.with_suffix(".pth")
+    if output_file.exists() and output_file.samefile(input_file):
+        raise RuntimeError("Input and output file are identical")
+    net = Net.load_from_checkpoint(input_file)
+    torch.save(net._model.state_dict(), output_file)
+
+
+if __name__ == "__main__":
+    typer.run(extract_unet)

scripts/make_datalist.py

@@ -0,0 +1,81 @@
+import json
+import random
+from pathlib import Path
+
+import typer
+
+from segmantic.image.labels import load_tissue_list
+from segmantic.utils.file_iterators import find_matching_files
+
+
+def make_datalist(
+    data_dir: Path = typer.Option(
+        ...,
+        help="root data directory. Paths in datalist will be relative to this directory",
+    ),
+    image_dir: Path = typer.Option(..., help="Directory containing images"),
+    labels_dir: Path = typer.Option(None, help="Directory containing labels"),
+    datalist_path: Path = typer.Option(..., help="Filename of output datalist"),
+    num_channels: int = 1,
+    num_classes: int = -1,
+    tissuelist_path: Path = None,
+    percent: float = 1.0,
+    description: str = "",
+    image_glob: str = "*.nii.gz",
+    labels_glob: str = "*.nii.gz",
+    test_only: bool = False,
+    seed: int = 104,
+) -> int:
+    # add labels
+    if tissuelist_path is not None:
+        tissuelist = load_tissue_list(tissuelist_path)
+        labels = {str(id): n for n, id in tissuelist.items() if id != 0}
+    elif num_classes > 0:
+        labels = {str(id): f"tissue{id:02d}" for id in range(1, num_classes + 1)}
+    else:
+        raise ValueError("Either specify 'tissuelist_path' or 'num_classes'")
+
+    data_config = {
+        "description": description,
+        "num_channels": num_channels,
+        "labels": labels,
+    }
+
+    # add all files as test files
+    if test_only:
+        test_files = (data_dir / image_dir).glob(image_glob)
+        data_config["training"] = []
+        data_config["validation"] = []
+        data_config["test"] = [str(f.relative_to(data_dir)) for f in test_files]
+
+    # build proper datalist with training/validation/test split
+    else:
+        matches = find_matching_files(
+            [data_dir / image_dir / image_glob, data_dir / labels_dir / labels_glob]
+        )
+        pairs = [
+            (p[0].relative_to(data_dir), p[1].relative_to(data_dir)) for p in matches
+        ]
+
+        random.Random(seed).shuffle(pairs)
+        test, pairs = pairs[:10], pairs[10:]
+        num_valid = int(percent * 0.2 * len(pairs))
+        num_training = len(pairs) - num_valid if percent >= 1.0 else 4 * num_valid
+
+        data_config["training"] = [
+            {"image": str(im), "label": str(lbl)} for im, lbl in pairs[:num_training]
+        ]
+        data_config["validation"] = [
+            {"image": str(im), "label": str(lbl)} for im, lbl in pairs[-num_valid:]
+        ]
+        data_config["test"] = ([str(im) for im, _ in test],)
+
+    return datalist_path.write_text(json.dumps(data_config, indent=2))
+
+
+def main():
+    typer.run(make_datalist)
+
+
+if __name__ == "__main__":
+    main()

scripts/visualize_label_surfaces.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Dict, List
 
 import itk
 import numpy as np
@@ -15,11 +14,11 @@ def extract_surfaces(
     file_path: Path,
     output_dir: Path,
     tissuelist_path: Path,
-    selected_tissues: List[int] = [],
+    selected_tissues: list[int] = [],
 ):
     image = itk.imread(f"{file_path}", pixel_type=itk.US)
 
-    tissues: Dict[int, str] = {}
+    tissues: dict[int, str] = {}
     if tissuelist_path.exists():
         name_id_map = load_tissue_list(tissuelist_path)
         tissues = {id: name for name, id in name_id_map.items()}

src/segmantic/__init__.py

@@ -1,4 +1,4 @@
 """ ML-based segmentation for medical images
 """
 
-__version__ = "0.3.0"
+__version__ = "0.4.0"