This release is a pre-release and may not be stable for production use.
pyplatypus
Computer vision for medical imaging — the engine behind the platypus R package.
0.3.0a5 — an alpha. This replaces the 2022 TensorFlow package with a PyTorch one. The API will still move and the R surface does not exist yet, so pin the exact version if you build on it.
Everything on PyPI so far is a pre-release, so
pip install pyplatypusresolves to this one.pip install pyplatypus==0.1.0rc2gets the old TensorFlow package.
What works today
Semantic segmentation in 2D, end to end:
- One spec, two ways in. Build it from arguments or load it from YAML — both produce the same object, so nothing downstream can tell which you used.
- Four architectures: U-Net, U-Net++, Res-U-Net, LinkNet, each composable with separable convolutions, spatial dropout, learned or interpolated upsampling, deep supervision and configurable block width.
- Nine losses (IoU, Dice, CCE, CCE-Dice, Focal, Tversky, Focal-Tversky, Combo, Lovász) and three metrics, all reducing over every axis except batch and channel — so they already work on volumes.
- Tiling that goes both ways: cut a large image into a grid instead of shrinking it, and get a full-size mask back.
- DICOM, read properly: stored values converted to real units through the modality LUT, a fixed window rather than one taken from the image, and MONOCHROME1 inverted. Opening the file is the easy part; those three are what make two scans comparable.
- Splitting by patient, not by slice:
split_dataset()turns one directory into train/validation/test CSVs, keeping every group whole. Slices of one patient on both sides of a split is the most common way to publish a Dice that means nothing, and agroup_bythat matches nothing is an error rather than a silent fallback. - Scores per case, not one number:
evaluate_cases()reports each image or patient separately andsummarise_cases()gives the distribution and names the worst one. On the Data Science Bowl a model at 0.855 over the split turned out to score 0.006 on three images; the mean had no way of saying so. - Volumes, not only slices: NIfTI in, 3D U-Net out, patches through the same tiling
that cuts 2D images. Every volume is reoriented to canonical (RAS) first - a NIfTI's
affine, not its array order, says where the anatomy is, and two datasets read naively can
be mirror images of each other. Masks may be label maps (
labels: [0, 1]) as well as pictures, because that is how every volume format stores them. - Resampling to a common voxel size:
target_spacing: [1, 1, 1]fixes the millimetres per voxel and then centre-crops or pads to the model'sinput_shape, instead of squeezing every scan into the same box. Clinical scans cover whatever length the question needed - 40 slices of 1 mm is 40 mm of patient, 40 slices of 2.5 mm is 100 mm - so resizing alone makes the same organ a different size in each and the network cannot tell. - One channel per file, in a stated order:
channels_fromtakes one pattern per channel, which is how BraTS ships four MRI sequences per patient and how satellite sets keep their bands. Sorting the names gives flair, t1, t1ce, t2 - reproducible, and anatomically meaningless; a model trained with FLAIR in channel one and used where channel one is T1 answers plausibly and reports nothing. A pattern matching two files, or none, or leaving a file unused, is an error. - Many models from one file, with a comparison table at the end.
Augmentation works in 3D, with a caveat the package handles rather than hides: albumentations
supports volumes unevenly - 97 of its transforms take one and the rest raise from inside the
library, GaussNoise as KeyError: 'images'. Every transform in a 3D specification is tried
against a small probe volume while the pipeline is built, so an unsupported one is named before
training starts, and available_transforms(rank=3) lists what is usable.
Object detection, ensembling and pretrained backbones remain out of scope.
Resampling is opt-in rather than automatic: it changes the voxel grid the model sees, which
is a decision to take deliberately. Without target_spacing the old behaviour stands and
volumes are resized to input_shape.
Try it
uv venv --python 3.11 .venv
uv pip install --python .venv/bin/python -e ".[dev]"
.venv/bin/python -m pytest
# The linter is pinned in pyproject.toml and installed on its own, so it agrees with CI
# and does not drag torch along to read text files.
uv run --only-group lint ruff check pyplatypus tests
from pyplatypus import Engine, from_yaml
engine = Engine(from_yaml("examples/data_science_bowl.yaml"))
engine.fit(verbose=True)
for row in engine.evaluate():
print(row)
masks = engine.predict(engine.best_model("dice"), split="test")
examples/data_science_bowl.yaml trains a U-Net and a LinkNet on the 2018 Data Science
Bowl and prints a comparison. On a GTX 1070 that is about 11 seconds per epoch at
160×160.
Requirements
Python ≥ 3.10, and torch ≥ 2.7.
If your GPU is a GTX 10-series (Pascal) or older, install the pascal extra:
pip install "pyplatypus[pascal]"
torch 2.8 and later ship CUDA 13 builds, and CUDA 13 dropped the Maxwell, Pascal and Volta generations outright - no driver update brings them back. The last torch built against CUDA 12 is 2.7.x, which the extra pins. On anything from Turing (RTX 20-series) onwards, ignore this.
Licence
MIT.
1 maintainer
Metadata
1 maintainer
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