mistmedical/mist repository overview

⁠Medical Imaging Segmentation Toolkit

MIST is a simple, scalable, end-to-end framework for 3D medical image segmentation. It handles everything from raw NIfTI files to trained models and evaluated predictions, with sensible defaults that work well out of the box and a configuration file for when you need more control.

⁠Installation

CPU (default — runs the full pipeline: train, predict, evaluate, everything):

pip install mist-medical

NVIDIA GPU (recommended — adds DALI-accelerated data loading):

pip install "mist-medical[dali]"

AMD ROCm GPU (install a ROCm-enabled PyTorch build first — PyPI's default torch wheel has no ROCm support — matching your driver's ROCm version, then MIST on top with no extra needed; see Accelerator support⁠ for details):

pip install torch --index-url https://download.pytorch.org/whl/rocm6.4
pip install mist-medical

Development:

git clone https://github.com/mist-medical/MIST.git
cd MIST
pip install -e .            # CPU
pip install -e ".[dali]"    # NVIDIA GPU acceleration (recommended on CUDA)

Docker (NVIDIA GPU required, driver ≥ 525.x):

docker pull mistmedical/mist:latest

⁠Quick Start

1. Prepare a dataset JSON describing your data:

{
  "task": "brats2023",
  "modality": "mr",
  "train-data": "/full/path/to/raw/data/train",
  "test-data": "/full/path/to/raw/data/validation",
  "mask": ["seg.nii.gz"],
  "images": {
    "t1": ["t1n.nii.gz"],
    "t2": ["t2w.nii.gz"],
    "tc": ["t1c.nii.gz"],
    "fl": ["t2f.nii.gz"]
  },
  "labels": [0, 1, 2, 3],
  "final_classes": { "WT": [1, 2, 3], "TC": [1, 3], "ET": [3] }
}

2. Run the full pipeline (analyze → preprocess → train → evaluate):

mist_run_all --data dataset.json \
             --numpy /path/to/numpy \
             --results /path/to/results

3. Run inference on new data:

mist_predict --models-dir /path/to/results/models \
             --config /path/to/results/config.json \
             --paths-csv /path/to/test.csv \
             --output /path/to/predictions

⁠Key Features

• Automatic configuration — analysis step determines target spacing, patch size, normalization, and foreground cropping from your data
• Five-fold cross-validation by default, with custom fold assignment support
• Multi-GPU training via PyTorch DDP on NVIDIA CUDA or AMD ROCm; uses all visible GPUs automatically
• GPU-accelerated data loading via NVIDIA DALI during training, with a generic CPU-based data loader used automatically on AMD ROCm or CPU-only hardware — no NVIDIA-specific dependencies required to train at all
• Sliding window inference with configurable overlap and patch blending
• Test-time augmentation and multi-model ensembling at inference
• Postprocessing with learnable, per-class morphological strategies
• CPU training and inference — the full pipeline, mist_train included, runs on any machine, including Macs and laptops without an NVIDIA GPU

⁠Supported Architectures

Change the architecture in config.json or pass --model <key> at the command line.

⁠Supported Loss Functions

Composite losses blend a region-based term with a boundary/distance term, weighted by a scheduled alpha. Schedules: constant, linear, cosine.

⁠Pipeline Commands

⁠Documentation

Full documentation, including configuration reference and advanced topics, is at mist-medical.readthedocs.io⁠.

⁠What's New

• November 2026 — MIST 2.4.0 — new ResEnc UNet (resenc), Primus, and PrimusV2 architectures, and fine-tuning from the openly available nnFoundationCNN and nnFoundationViT pretrained encoders with --pretrained-weights. Try it in the fine-tuning notebook⁠.
• October 2026 — MIST 2.3.0, the first stable release — CPU and AMD ROCm training, mist_finalize for per-fold distributed training, probability-level and parallel ensembling, and no more ANTs dependency. pip install --upgrade mist-medical, or pip install --upgrade "mist-medical[dali]" for DALI-accelerated data loading on NVIDIA GPUs (highly recommended).
• July 2026 — Interactive Colab demo — run the full pipeline on a free Colab GPU with the heart segmentation notebook⁠.
• 2024 and 2025 — 3rd place in the BraTS adult glioma challenge at MICCAI 2024⁠ and MICCAI 2025⁠.

See the changelog⁠ for the full release history.

⁠Citation

If you use MIST in your work, please cite:

@article{celaya2024mist,
  title   = {MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework},
  author  = {Celaya, Adrian and others},
  journal = {arXiv preprint arXiv:2407.21343},
  year    = {2024}
}

@article{celaya2022pocketnet,
  title   = {PocketNet: A Smaller Neural Network For Medical Image Analysis},
  author  = {Celaya, Adrian and others},
  journal = {IEEE Transactions on Medical Imaging},
  doi     = {10.1109/TMI.2022.3224873},
  year    = {2022}
}