Multi-Scale Fusion for Real-Time Image Observation and Data Analysis of Athletes after Soft Tissue Injury.

IF 1.1 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Medical Imaging Reviews Pub Date : 2025-10-15 DOI:10.2174/0115734056403181250925102654

Jinhui Li, Yang Yu, Jiaxing Han

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引用次数: 0

Abstract

Objective: To address insufficient segmentation accuracy in athletes' soft tissue injury analysis, this study proposes an enhanced Swin-Unet model with multiscale feature fusion via the FPN (Feature Pyramid Network) and an adaptive window selection mechanism for dynamic receptive field adjustment.

Methods: A weighted hybrid loss function integrating Dice Loss, Cross-Entropy Loss, and boundary auxiliary loss optimizes segmentation precision and boundary recognition.

Results: Evaluated on the OAI-ZIB dataset using 10-fold cross-validation, the model achieves a DSC (Dice Similarity Coefficient) of 0.978, outperforming baseline Swin-Unet and mainstream architectures. Superior performance is demonstrated in IoU (Intersection over Union) (0.968) and boundary Hausdorff distance (3.21), alongside significantly reduced diagnosis time (6.0 minutes vs. 16.8 minutes manually).

Conclusion: This framework enhances real-time medical imaging analysis for athlete injuries, offering improved accuracy, efficiency, and clinical utility in soft tissue segmentation tasks.

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运动员软组织损伤后实时图像观察与数据分析的多尺度融合。

目的：针对运动员软组织损伤分析中分割精度不足的问题，提出了一种基于特征金字塔网络（feature Pyramid Network， FPN）的多尺度特征融合的swun - unet模型和动态感受野调整的自适应窗口选择机制。方法：采用加权混合损失函数，将骰子损失、交叉熵损失和边界辅助损失相结合，优化分割精度和边界识别。结果：在OAI-ZIB数据集上进行10倍交叉验证，该模型的DSC （Dice Similarity Coefficient）达到0.978，优于基准swing - unet和主流架构。在IoU (Intersection over Union)（0.968）和边界Hausdorff距离（3.21）方面表现优异，同时显著缩短了诊断时间（6.0分钟，而人工诊断时间为16.8分钟）。结论：该框架增强了对运动员损伤的实时医学成像分析，提高了软组织分割任务的准确性、效率和临床实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Medical Imaging Reviews 医学-核医学

CiteScore

2.60

自引率

0.00%

发文量

246

审稿时长

1 months

期刊介绍： Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.