Segmentation of the distal femur in ultrasound images

Abstract

Abstract Objectives Ultrasound is a widely used imaging technology that allows for fast diagnosis of a broad range of illnesses and injuries of the musculoskeletal system. However, interpreting ultrasound images remains a challenging task that requires expert knowledge and years of training for each exam. One crucial step for the long-term goal of automatic diagnosis is pixel wise semantic segmentation. Methods In this work, several state-of-the-art semantic segmentation networks were trained on a new dataset of manually annotated ultrasound images depicting the distal femur. Results PSP-Net achieved the best overall performance with an average surface distance error (SDE) of 0.64 mm. Conclusions We recommend the PSP-Net architecture for semantic segmentation of bone surfaces.

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