MIST

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MIST: A simple and scalable end-to-end framework for 3D medical imaging segmentation.

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Medical Imaging Segmentation Toolkit
===

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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.

> [!TIP]
> **Try MIST in your browser — no setup required.** The
> [**end-to-end Colab demo**](https://colab.research.google.com/github/mist-medical/MIST/blob/main/examples/mist_heart_demo.ipynb)
> runs the whole pipeline — analyze → preprocess → train → evaluate → predict →
> postprocess → rank → visualize — on a heart MRI dataset using a free Colab T4
> GPU.
>
> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mist-medical/MIST/blob/main/examples/mist_heart_demo.ipynb)

## Installation

**Training** (NVIDIA GPU required):
```bash
pip install "mist-medical[train]"
```

**Inference only** (CPU-compatible):
```bash
pip install mist-medical
```

**Development**:
```bash
git clone https://github.com/mist-medical/MIST.git
cd MIST
pip install -e ".[train]"    # full
pip install -e .             # inference only
```

**Docker** (NVIDIA GPU required, driver ≥ 525.x):
```bash
docker pull mistmedical/mist:latest
```

## Quick Start

**1. Prepare a dataset JSON** describing your data:
```json
{
    "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):
```bash
mist_run_all --data dataset.json \
             --numpy /path/to/numpy \
             --results /path/to/results
```

**3. Run inference** on new data:
```bash
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; 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
- **Postprocessing** with learnable, per-class morphological strategies
- **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` |

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

## Supported Loss Functions

| Loss | Key | Notes |
|---|---|---|
| Dice | `dice` | |
| Dice + Cross-Entropy | `dice_ce` | |
| clDice | `cldice` | Composite |
| Boundary Loss | `bl` | Composite |
| Generalized Surface Loss | `gsl` | Composite |
| Hausdorff Distance One-Sided | `hdos` | Composite |
| Volumetric SDDL | `volumetric_sddl` | Composite |
| Vessel SDDL | `vessel_sddl` | Composite |

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

## Pipeline Commands

| Command | Description |
|---|---|
| `mist_run_all` | Run the full pipeline end-to-end |
| `mist_analyze` | Analyze dataset and generate `config.json` |
| `mist_preprocess` | Preprocess images into NumPy arrays |
| `mist_train` | Train models |
| `mist_predict` | Run inference on new data |
| `mist_ensemble` | Combine predictions from multiple models via STAPLE or majority vote |
| `mist_evaluate` | Evaluate predictions against ground truth |
| `mist_postprocess` | Apply postprocessing strategies |
| `mist_rank` | Rank multiple evaluation result CSVs BraTS-style |
| `mist_average_weights` | Average model weights across folds |
| `mist_convert_msd` | Convert Medical Segmentation Decathlon datasets |
| `mist_convert_csv` | Convert CSV-formatted datasets |

## Documentation

Full documentation, including configuration reference and advanced topics, is at
[**mist-medical.readthedocs.io**](https://mist-medical.readthedocs.io/).

## What's New

* July 2026 — **Interactive Colab demo** — run the full MIST pipeline end-to-end
  on a free Colab GPU with the
  [heart segmentation notebook](https://colab.research.google.com/github/mist-medical/MIST/blob/main/examples/mist_heart_demo.ipynb):
  analysis, training, evaluation, inference, postprocessing, and BraTS-style
  ranking, all in the browser with no local setup.
* June 2026 — **Official Docker image** — `mistmedical/mist:latest` ships a
  CUDA 12.8 build. Requires NVIDIA driver ≥ 525.x.
* June 2026 — **2.0.1 release candidate** — BF16 automatic mixed precision
  replaces FP16 throughout training and inference, reducing memory use and
  eliminating gradient loss scaling. Sliding-window inference gains a tunable
  `sw_batch_size` parameter. `mist_rank` adds pairwise Wilcoxon significance
  testing via `--significance-csv`. Several targeted memory-reduction fixes
  land across the inference stack.
* June 2026 — **Multi-model ensembling** — `mist_ensemble` combines discrete
  NIfTI predictions from two or more separately trained models into a single
  consensus segmentation via STAPLE (`--ensemble-backend staple`, default) or
  majority vote (`--ensemble-backend majority_vote`). Works for single-class and
  multi-class label maps.
* May 2026 — **2.0.0 release candidate** — BraTS-style multi-strategy ranking
  (`mist_rank`), a structured postprocessing transform registry with LLM-readable
  metadata (`describe_transforms`), and full pathlib + PEP 585/604 modernization
  across the codebase.
* 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](https://www.synapse.org/Synapse:syn64153130/wiki/633062)** — 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](https://www.synapse.org/Synapse:syn53708249/wiki/630150)** — MIST takes 3rd place.

## Citation

If you use MIST in your work, please cite:

```bibtex
@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}
}
```

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