MOD: Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

🎉 This work is published in IEEE Journal of Biomedical and Health Informatics

Network Architecture

Data Description

Dataset Name: BraTS2021

Modality: MRI

Size: 1470 3D volumes (1251 Training + 219 Validation)

Challenge: RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge

• Register and download the official BraTS 21 dataset from the link below and place then into "TrainingData" in the dataset folder:

  https://www.synapse.org/#!Synapse:syn27046444/wiki/616992

  For example, the address of a single file is as follows:

  "TrainingData/BraTS2021_01146/BraTS2021_01146_flair.nii.gz"

• Download the json file from this link and placed in the same folder as the dataset.

The sub-regions considered for evaluation in BraTS 21 challenge are the "enhancing tumor" (ET), the "tumor core" (TC), and the "whole tumor" (WT). The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (NCR) parts of the tumor. The appearance of NCR is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edematous/invaded tissue (ED), which is typically depicted by hyper-intense signal in FLAIR [BraTS 21].

Visualization

Training

Install Environment

pip install -r requirements.txt

Training Script

The mod-config.yml is the global parameters control file. Dataset loading and related parameter selection are controlled through the three bool variables of is_brats2021, is_brats2019 in mod-config.yml.

Train UNETR

accelecate launch mod_unetr.py

Train ViT

accelecate launch mod_vit.py

Bixtex

@article{pang2025online,
  title={Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation},
  author={Pang, Yan and Li, Yunhao and Huang, Teng and Liang, Jiaming and Wang, Zhen and Dong, Changyu and Kuang, Dongyang and Hu, Ying and Chen, Hao and Lei, Tim and others},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2025},
  publisher={IEEE}
}