  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.
