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Medsegdiff

Medsegdiff is an open-source Python framework that applies diffusion probabilistic models to segment and reconstruct organs or tissues in medical images. The repository describes MedSegDiff-V1 and a transformer-based MedSegDiff-V2, and provides scripts for training, sampling predictions and evaluating image segmentations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Medsegdiff — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-based starting point for experimenting with diffusion-model segmentation or reconstruction and can prepare the data and run the repository’s Python scripts.

Who it is for

  • Medical-imaging researchers experimenting with diffusion-based segmentation.
  • Developers building research workflows for organ, tissue or brain-tumor image segmentation.

Documented uses

  • Train a segmentation model using the documented ISIC skin-image or BRATS2020 brain MRI examples.
  • Generate segmentation predictions from medical images using a saved model.
  • Evaluate saved prediction images against ground-truth images with the documented ISIC evaluation script.
Before you choose

Requirements & limits

The repository is public, identifies an MIT license, and documents installation through `pip install -r requirement.txt`.

  • The project is a Python code framework, not a documented hosted API service.
  • The README provides specific data layouts and command-line examples for ISIC and BRATS2020 workflows.
  • Sampling requires a saved model; the examples also specify model and diffusion parameters.
  • The repository describes a transformer-based V2 alongside V1.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train and sample an ISIC skin-image segmentation model

Use the documented ISIC example to train a model and generate segmentation predictions.

Before you start

  • Python environment with dependencies installed using pip install -r requirement.txt.
  • ISIC dataset arranged under data/ISIC with the documented Train and Test image, ground-truth and CSV folders.

Steps

  1. Install the repository dependencies with the command below.

    Documented command
    pip install -r requirement.txt

  2. Place the ISIC dataset in the documented folder layout under data/ISIC.

  3. Run the command below with --data_name ISIC, the dataset path, an output path and the example’s model and training parameters.

    Documented command
    python scripts/segmentation_train.py

  4. Run the command below with the ISIC dataset path, output path, saved model path and the example’s sampling parameters.

    Documented command
    python scripts/segmentation_sample.py

Expected outcome

The training stage writes model outputs to the chosen output path; the sampling stage saves prediction images, with the README noting `./results/` as the default sample location.

Guide limitations

  • The guide covers training and sampling, not execution verification or clinical validation.
  • The dataset must be obtained separately from the ISIC challenge site.
  • The README’s example uses specific image and model parameters; it does not establish suitability for other datasets or settings.
Documentation 1 ↗
Source-based practical guide

Train and sample a BRATS2020 brain-tumor segmentation model

Follow the repository’s documented BRATS2020 MRI example through model training and prediction sampling.

Before you start

  • Python environment with repository dependencies installed using pip install -r requirement.txt.
  • BRATS2020 data arranged in the documented training and testing folders with the listed MRI modalities and segmentation files.

Steps

  1. Install dependencies with the command below.

    Documented command
    pip install -r requirement.txt

  2. Download and arrange BRATS2020 data in the documented training and testing folder structure.

  3. Run the command below with the training data directory, output path and the README’s example training parameters.

    Documented command
    python scripts/segmentation_train.py

  4. Run the command below with the testing data directory, output path, saved model path and the example sampling parameters.

    Documented command
    python scripts/segmentation_sample.py

Expected outcome

The documented workflow produces a trained model and sampled brain-tumor segmentation predictions in the chosen output location.

Guide limitations

  • The README example does not specify evaluation steps for this BRATS workflow.
  • Dataset access and preparation are separate from the repository.
  • The listed procedure is not evidence of clinical validation or compatibility with other MRI datasets.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
ImprintLab/MedSegDiff · Official homepage title · retained in its source language
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