MTTU-Net

Code for "A Fully Automated Multimodal MRI-based Multi-task Learning for Glioma Segmentation and IDH Genotyping"

Cite bibtex format

@ARTICLE{cheng2022idh,
author={Cheng, Jianhong and Liu, Jin and Kuang, Hulin and Wang, Jianxin},
journal={IEEE Transactions on Medical Imaging},
title={A Fully Automated Multimodal MRI-based Multi-task Learning for Glioma Segmentation and IDH Genotyping},
year={2022},
doi={10.1109/TMI.2022.3142321}}

Prerequisites

Python 3.7+

Pytorch 1.7.0+

This code has been tested with Pytorch 1.7.0 and two NVIDIA V100 GPU.

Data descriptions

The data used in this study includes the MRI data and IDH genomic information. MRI data are derived from BraTS 2020 and The Cancer Imaging Archive, and the corresponding genomic information is from The Cancer Genome Atlas. We have provided the name mapping between BraTS 2020 and TCIA, which can been found in the data directory "MTTU-Net/data/".

Training the model

python -m torch.distributed.launch --nproc_per_node=2 --master_port 22 train_IDH.py

Inference

python test.py