Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition

Acoustic bone shadow information in ultrasound (US) is important during imaging bones in US-guided orthopedic procedures. In this work, an end to end deep learning-based method is proposed to segment the bone shadow region from US data. In particular, we decompose the bone shadow segmentation task into two subtasks, coarse bone shadow enhancement (BSE) and horizontal bone interval mask (HBIM) estimation. Outputs from two subtasks are processed by a masking operation to generate the final bone shadow segmentation. To better leverage the mutual information in different tasks, our model features a shared encoder as deep feature extractor for both subtasks and two multi-scale pyramid pooling decoders. Additionally, we propose a conditional shape discriminator to regularize the shape of the output segmentation map. The proposed method is validated on 814 in vivo US scans obtained from knee, femur, distal radius and tibia bones. Validation against expert annotation achieved statistically significant improvements in segmentation of bone shadow regions compared to the state-of-the-art method.