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.
@article{arxiv.2212.10724,
title = {Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation},
author = {Ye Li and Junyu Chen and Se-in Jang and Kuang Gong and Quanzheng Li},
journal= {arXiv preprint arXiv:2212.10724},
year = {2022}
}
Comments
Accepted for oral presentation by IEEE Medical Imaging Conference 2022