bibtext: @InProceedings{ Che_HybridStructureOriented_MICCAI2024,
                   author = { Chen, Lingyu and Wang, Yue and Zhao, Zhe and Liao, Hongen and Zhang, Daoqiang and Han, Haojie and Chen, Fang },
                   title = { { Hybrid-Structure-Oriented Transformer for Arm Musculoskeletal Ultrasound Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Segmenting complex layer structures, including subcutaneous fat, skeletal muscle, and bone in arm musculoskeletal ultrasound (MSKUS), is vital for diagnosing and monitoring the progression of Breast-Cancer-Related Lymphedema (BCRL). Nevertheless, previous researches primarily focus on individual muscle or bone segmentation in MSKUS, overlooking the intricate and hybrid-layer morphology that characterizes these structures. To address this limitation, we propose a novel approach called the hybrid structure-oriented Transformer (HSformer), which effectively captures hierarchical structures with diverse morphology in MSKUS. Specifically, HSformer combines a hierarchical-consistency relative position encoding and a structure-biased constraint for hierarchical structure attention. Our experiments on arm MSKUS datasets demonstrate that HSformer achieves state-of-the-art performance in segmenting subcutaneous fat, skeletal muscle and bone.
title: Hybrid-Structure-Oriented Transformer for Arm Musculoskeletal Ultrasound Segmentation
authors: Chen, Lingyu Wang, Yue Zhao, Zhe Liao, Hongen Zhang, Daoqiang Han, Haojie Chen, Fang