Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning

Abstract

Wrist fractures are among the most common upper limb injuries in children and are currently examined by X-rays. Ultrasound imaging offers a radiation-free alternative that can be used for a fast and effective evaluation by assessing the severity of suspected fractures before further referral to X-rays. This project proposes a segmentation framework based on the nnU-Net model to segment bony structures such as the epiphysis and metaphysis commonly seen in wrist ultrasound images. As a preprocessing step, we use an image enhancement technique such as Contrast-Limited Adaptive Histogram Equalization (CLAHE) and report the accuracy of segmentation with and without preprocessing.Experiments were conducted on 16,865 training and 3,822 testing ultrasound images from 74 and 18 subjects, respectively. The results show that training and testing on CLAHE-enhanced images improves segmentation performance, achieving a DICE score of 0.874 compared to 0.872 without pre-processing.Clinical relevance-This study shows the feasibility of using automatic segmentation of wrist ultrasound images acquired by lightly trained users in a pediatric emergency setting. It combines CLAHE-based image enhancement with semantic segmentation using the nnUNet-inspired model to segment the epiphysis, metaphysis, and carpal bone from wrist ultrasound images. In an emergency care setting, this approach could be integrated into an effective triage tool to assess the severity of pediatric wrist injuries and reduce the need for x-ray examination in cases with no fractures.

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