Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
This work addresses segmentation of head-and-neck tumors for medical imaging, but it is incremental as it evaluates existing methods without introducing new ones.
The study compared Transformer-based networks to nnU-Net for multimodal head-and-neck tumor segmentation, finding that U-Net performed better for small structures with limited data and resources.
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation. In this study, we analyze, two recently published Transformer-based network architectures for the task of multimodal head-and-tumor segmentation and compare their performance to the de facto standard 3D segmentation network - the nnU-Net. Our results showed that modeling long-range dependencies may be helpful in cases where large structures are present and/or large field of view is needed. However, for small structures such as head-and-neck tumor, the convolution-based U-Net architecture seemed to perform well, especially when training dataset is small and computational resource is limited.