Real-Time Segmentation of B-Mode Ultrasound Images for Muscle Strain Using Deep Learning

Abstract

Objective

The diagnosis of muscle strain using B-mode ultrasound is highly dependent on the sonographer’s experience, which can lead to diagnostic errors and prolonged examination times. This study aims to develop a real-time deep learning algorithm for the segmentation of muscle strain in B-mode ultrasound images, designed to assist sonographers by enhancing diagnostic accuracy and efficiency.

Methods

A novel real-time instance segmentation model was proposed based on the YOLACT architecture. Key innovations include the integration of a VoVNet backbone for efficient feature extraction, an anchor-free prediction head incorporating multireceptive fields and a layer attention mechanism, and the application of deformable convolutions for mask coefficient prediction. The model was guided by a composite loss function, featuring an improved focal loss with soft labels to align classification and localization quality, and a distribution focal loss to enhance robustness against ambiguous annotations. The algorithm was trained and validated on a custom dataset of 1516 B-mode ultrasound images from 183 patients.

Results

The proposed algorithm achieved state-of-the-art performance, yielding a bounding box AP50 of 81.8 and a mask AP50 of 81.4. Crucially, it demonstrated real-time capability with a processing speed of 40.9 frames per second (fps) on a GeForce RTX 4090 GPU. The model’s performance surpassed that of standard object detection and segmentation benchmarks. Ablation studies systematically validated the significant contribution of each methodological innovation to the final performance.

Conclusion

This study successfully developed an efficient and accurate algorithm for the real-time segmentation of muscle strain in ultrasound images. Its high speed and accuracy demonstrate its potential as a valuable computer-aided diagnostic tool to support sonographers in clinical practice, ultimately aiming to improve the utilization of medical resources and enhance patient care.