TRUNet

Pre-print: here

Semantic segmentation for 3D volume images using a modified ResNet50 v2 block and a Vision Transformer Block in a U-Net framework.

This is an adaptation of TransUNet for 3D inputs. Instead of the CNN encoder used in TransUNet the "Hybrid" approach including a modified RedNet50 block proposed by the authors is used.

Installation:

• Clone repository: git clone https://github.com/ljollans/TRUNet.git
• If you want to create a new virtual environment:
  • python3 -m venv ./venv
  • source ./venv/bin/activate
• Install requirements: pip install -r requirements.txt

[22nd August 2023] currently the cardiac segmentation model trained using TRUNet is not available because of its large size

Related work:

Li, Dapeng, et al. "A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification." Scientific Reports 13.1 (2023): 13529.

Chen, Jieneng, et al. "3d transunet: Advancing medical image segmentation through vision transformers." arXiv preprint arXiv:2310.07781 (2023).

Yang, Siwei, et al. "3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge." arXiv preprint arXiv:2403.15735 (2024).