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

Training (NVIDIA GPU required):

pip install "mist-medical[train]"

Inference only (CPU-compatible, works on Mac):

pip install mist-medical

Key Features

  • Automatic configuration — analysis step determines target spacing, patch size, normalization, and foreground cropping from your data and available GPU memory
  • Five-fold cross-validation by default, with custom fold assignment support
  • Multi-GPU training via PyTorch DDP; uses all visible GPUs automatically
  • GPU-accelerated data loading via NVIDIA DALI during training
  • Sliding window inference with configurable overlap and patch blending
  • Test-time augmentation and multi-model ensembling at inference
  • Transfer learning — initialize encoders from pretrained weights
  • Resume training — continue interrupted runs from the last checkpoint
  • CPU inference — mist_predict runs on any machine, including Macs

Supported Architectures

Model Key
nnU-Net nnunet
nnU-Net Pocket nnunet-pocket
MedNeXt (small / base / medium / large) mednext-small, mednext-base, mednext-medium, mednext-large
FMG-Net fmgnet
W-Net wnet
Swin UNETR (small / base / large) swinunetr-small, swinunetr-base, swinunetr-large

What's New

  • April 2026 — CPU inference support — mist_predict now runs on any machine, including Macs and laptops without an NVIDIA GPU. Install with pip install mist-medical (no GPU required).
  • March 2026 — Resume training — interrupted runs can be continued from the last checkpoint with --resume, with atomic checkpointing to prevent corruption.
  • March 2026 — GPU-aware automatic patch size — the analysis step now derives the patch size from available GPU memory, so the default configuration is hardware-appropriate without manual tuning.
  • March 2026 — Transfer learning — initialize encoders from pretrained weights with --pretrained-weights, and average model weights across folds with mist_average_weights.
  • March 2026 — Better training defaults — AdamW optimizer and gradient clipping are now the defaults, with the clipping threshold exposed via grad_clip_norm in config.json.
  • September 2025 — BraTS 2025 adult glioma challenge @ MICCAI 2025 — MIST takes 3rd place (repeat).
  • November 2024 — MedNeXt models — small, base, medium, and large variants added (mednext-small, mednext-base, mednext-medium, mednext-large).
  • October 2024 — BraTS 2024 adult glioma challenge @ MICCAI 2024 — MIST takes 3rd place.

Citation

If you use MIST in your work, please cite:

A. Celaya et al. "MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework," arXiv preprint arXiv:2407.21343

A. Celaya et al., "PocketNet: A Smaller Neural Network For Medical Image Analysis," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3224873.