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Mednext

MedNeXt is a Python implementation of a fully ConvNeXt architecture for 3D medical image segmentation, designed for sparsely annotated medical imaging datasets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mednext — 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 MedNeXt when developing or adapting a 3D medical-image segmentation model and you want either the repository’s nnU-Net v1-based training framework or an architecture that can be imported into an external PyTorch pipeline.

Who it is for

  • Researchers developing 3D medical-image segmentation models.
  • Developers integrating a segmentation architecture or reusable network blocks into a PyTorch training pipeline.
  • Teams exploring the repository’s nnU-Net v1-based training framework.

Documented uses

  • Build a training workflow for 3D volumetric segmentation using the complete MedNeXt architecture.
  • Use individual MedNeXt blocks in a separate network or training pipeline.
  • Explore model configurations and training approaches for sparsely annotated medical-image datasets.
Before you choose

Requirements & limits

  • The included training framework is built on nnU-Net v1; using MedNeXt as an architecture in an external pipeline is described as decoupled from nnU-Net.
  • MedNeXt v1 was trained with 1.0 mm isotropic spacing. Other spacing is described as usable in theory, but untested and potentially performance-affecting.
  • Gradient checkpointing trades computation for activation storage when training larger models.

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

Install the MedNeXt repository

Install the repository in editable mode as the documented starting point for using its architecture or training pipeline.

Before you start

  • Git
  • Python and pip

Steps

  1. Clone the MedNeXt repository into a directory named mednext.

  2. Change into the mednext directory.

  3. Run pip install -e .

Expected outcome

The repository is installed in editable mode, ready for the documented architecture or pipeline workflow.

Guide limitations

  • This installation procedure does not itself configure or run a training job.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
MIC-DKFZ/MedNeXt · Official homepage title · retained in its source language
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