DeepMedic

Use this skill when a task involves the DeepMedic 0.8.4 package, its deepMedicRun CLI, Python-syntax configuration files, multi-modal NIFTI volumes, multi-scale 3-D CNN segmentation, TensorFlow checkpoints, or medical-imaging training and inference. This graph is for operating the installed package; it does not require the original source checkout.

First route

1. Establish the dataset contract before writing configs. Read data preparation for NIFTI shape, affine/voxel-size, modality order, labels, ROI, list files, CSV input, and normalization checks.
2. Author or resize the network with model architecture. Keep output classes, input channels, pathways, receptive fields, and segment dimensions compatible with every checkpoint and dataset.
3. Configure and launch supervised learning with training. It covers sampling, validation, augmentation, optimizers, schedules, checkpoints, resume/fine-tune, and progress logs.
4. Segment unseen volumes with inference. It covers TestConfig, checkpoint loading, tiling, ROI restriction, probability/feature outputs, DSC, and output validation.
5. For an issue that crosses routes, read the shared troubleshooting reference first, then the nearest sub-skill troubleshooting file.

Installation and environment contract

DeepMedic's package metadata installs nibabel, numpy, scipy, six, and pandas, but it does not declare TensorFlow. Install a TensorFlow 2.x build separately and select versions that agree with the Python, NumPy, protobuf, CUDA, and cuDNN versions on the host. The verified revision was operated with Python 3.8, TensorFlow 2.6.2, NumPy 1.19.5, SciPy 1.7.3, pandas 1.2.5, NiBabel 3.2.2, protobuf 3.20.3, and Matplotlib 3.5.3. Treat those as a compatibility baseline, not as a universal requirement.

After installing the package, run the following safe checks:

python -c "import deepmedic, tensorflow as tf, nibabel; print(tf.__version__)"
deepMedicRun -h
python path/to/plot_training_progress.py -h
python path/to/scripts/check_environment.py --json

Use the bundled environment checker for a read-only import, version, TensorFlow, and deepMedicRun -h diagnostic; it is safe to run from any working directory.

For CUDA, install a TensorFlow build and CUDA/cuDNN runtime compatible with the host driver; then verify a device operation. A requested -dev cuda flag is not proof that CUDA was used. Read the session log and monitor device assignment. If the host is CPU-only, use -dev cpu for small smoke tests and do not claim GPU coverage.

Cross-cutting invariants

• Config files are executed as Python. Treat them as trusted, reviewable input; do not execute untrusted configuration files merely to inspect them.
• NIFTI channels, labels, and ROI masks for one subject must be co-registered and have the same array shape and voxel size. Keep modality list order fixed.
• Labels use background 0 and contiguous increasing class ids. The model's numberOfOutputClasses includes background.
• A checkpoint is a prefix ending in .model.ckpt; do not pass its .index or .data-* companion as -load. The architecture config must match the checkpoint's variable names and shapes.
• Large 3-D segments, full-volume validation, feature-map saving, and the published DeepMedic model are expensive. Start with the tiny configuration, reduce cases/epochs/batches, and make output paths explicit.
• Keep runtime outputs outside the skill directory. Check logs, checkpoint completeness, NIFTI geometry, finite probabilities, and expected case names before handing results downstream.

Provenance and staleness

Read repo-provenance.md when comparing this skill with a newer DeepMedic checkout. If the source commit, package version, public entry point, or config behavior changes, use a refresh workflow rather than assuming this graph is current. The structured router metadata is consumed by the managed repo-skill importer and should not be hand-edited into router prose.

Scope boundary

This skill teaches DeepMedic operation, not medical diagnosis, annotation quality, registration/resampling software, or clinical deployment. It does not bundle the repository's large example NIFTI data, model checkpoints, generated logs, or image assets. Use a trusted imaging toolchain to create and QA those inputs, then hand the resulting manifests to data-preparation.