Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation

• URL: http://arxiv.org/abs/2212.10724v1
• Date: Wed, 21 Dec 2022 02:35:46 GMT
• Title: Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
• Authors: Ye Li, Junyu Chen, Se-in Jang, Kuang Gong, Quanzheng Li
• Abstract summary: In this study, we analyze, two recently published Transformer-based network architectures for the task of multimodal head-and-tumor segmentation. 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. 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.
• Score: 9.441769048218955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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