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.