  Semantic segmentation of brain tumors is a fundamental medical image analysis
task involving multiple MRI imaging modalities that can assist clinicians in
diagnosing the patient and successively studying the progression of the
malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs)
approaches have become the de facto standard for 3D medical image segmentation.
The popular "U-shaped" network architecture has achieved state-of-the-art
performance benchmarks on different 2D and 3D semantic segmentation tasks and
across various imaging modalities. However, due to the limited kernel size of
convolution layers in FCNNs, their performance of modeling long-range
information is sub-optimal, and this can lead to deficiencies in the
segmentation of tumors with variable sizes. On the other hand, transformer
models have demonstrated excellent capabilities in capturing such long-range
information in multiple domains, including natural language processing and
computer vision. Inspired by the success of vision transformers and their
variants, we propose a novel segmentation model termed Swin UNEt TRansformers
(Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is
reformulated as a sequence to sequence prediction problem wherein multi-modal
input data is projected into a 1D sequence of embedding and used as an input to
a hierarchical Swin transformer as the encoder. The swin transformer encoder
extracts features at five different resolutions by utilizing shifted windows
for computing self-attention and is connected to an FCNN-based decoder at each
resolution via skip connections. We have participated in BraTS 2021
segmentation challenge, and our proposed model ranks among the top-performing
approaches in the validation phase. Code: https://monai.io/research/swin-unetr
