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Segformer3D

SegFormer3D is an open-source Python implementation of a hierarchical Transformer architecture that segments 3D volumetric medical images by aggregating multiscale features with an all-MLP decoder.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Segformer3D — 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 need a code-based research implementation for 3D medical-image segmentation and your workflow can follow the repository's dataset and environment instructions.

Who it is for

  • Medical-imaging researchers working on 3D segmentation
  • Developers building or evaluating volumetric segmentation experiments

Documented uses

  • Prepare BraTS volumes for model experiments
  • Build a segmentation workflow around a hierarchical Transformer
  • Evaluate model outputs in medical-imaging research
Before you choose

Requirements & limits

The implementation is published in a public GitHub repository under the GPL-3.0 license.

  • The documented environment specifies Conda, Python 3.11.7, PyTorch 2.1.0 and CUDA 11.8 packages.
  • BraTS 2017 preprocessing requires about 22 GB for processed data.
  • The README says ACDC and Synapse processing code uses the nnFormer repository.

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

Prepare BraTS 2017 volumes for an experiment

Install the documented environment and preprocess BraTS 2017 data into the repository's training-data folder.

Before you start

  • Conda
  • BraTS 2017 dataset archive
  • About 22 GB of storage for preprocessed data

Steps

  1. Clone the SegFormer3D repository and create its Conda environment with Python 3.11.7.

  2. Install the documented PyTorch 2.1.0, torchvision 0.16.0, torchaudio 2.1.0 and CUDA 11.8 packages, then install requirements.txt.

  3. Create the documented raw-data and BraTS2017_Training_Data directories and extract the dataset into the raw-data train directory.

  4. Run data/brats2017_seg/brats2017_raw_data/brats2017_seg_preprocess.py from the repository.

Expected outcome

Preprocessed BraTS 2017 volumes are saved in BraTS2017_Training_Data for use in the training workflow.

Guide limitations

  • The preprocessing step takes time and requires about 22 GB for the processed data.
  • This recipe covers data preparation; it does not provide experiment execution steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
15 October 2026
Original website title
OSUPCVLab/SegFormer3D · Official homepage title · retained in its source language
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