BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation
arXiv:2109.12271 · doi:10.1007/978-3-031-09002-8_1
Abstract
Convolutional neural networks (CNNs) have achieved remarkable success in automatically segmenting organs or lesions on 3D medical images. Recently, vision transformer networks have exhibited exceptional performance in 2D image classification tasks. Compared with CNNs, transformer networks have an appealing advantage of extracting long-range features due to their self-attention algorithm. Therefore, we propose a CNN-Transformer combined model, called BiTr-Unet, with specific modifications for brain tumor segmentation on multi-modal MRI scans. Our BiTr-Unet achieves good performance on the BraTS2021 validation dataset with median Dice score 0.9335, 0.9304 and 0.8899, and median Hausdorff distance 2.8284, 2.2361 and 1.4142 for the whole tumor, tumor core, and enhancing tumor, respectively. On the BraTS2021 testing dataset, the corresponding results are 0.9257, 0.9350 and 0.8874 for Dice score, and 3, 2.2361 and 1.4142 for Hausdorff distance. The code is publicly available at https://github.com/JustaTinyDot/BiTr-Unet.
Accepted by MICCAI BrainLes 2021
References in corpus (8)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- UNETR: Transformers for 3D Medical Image Segmentation
- CvT: Introducing Convolutions to Vision Transformers
- The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
- CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
- CAT: Cross Attention in Vision Transformer