复杂视网膜显微手术中器械检测方法  

An instrument detection method for complex retinal microsurgery

作　　者：何昱昊 陈一巍 樊金宇 何益 史国华 He Yuhao;Chen Yiwei;Fan Jinyu;He Yi;Shi Guohua(Department of Biomedical Engineering,University of Science and Technology of China,Hefei,Anhui 230026,China;Jiangsu Key Laboratory of Medical Optics,Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou,Jiangsu 215163,China)

机构地区：[1]中国科学技术大学生物医学工程学院,安徽合肥230026 [2]中国科学院苏州生物医学工程技术研究所江苏省医用光学重点实验室,江苏苏州215163

出　　处：《光电工程》2025年第2期68-81,共14页Opto-Electronic Engineering

基　　金：国家重点研发计划(2021YFF0700503,2022YFC2404201);中国科学院稳定支持基础研究领域青年团队计划(YSBR-067);江苏省科技计划项目(BK20220263);苏州市姑苏创新创业领军人才(ZXL2021425);苏州市基础研究试点项目(SSD2023018)。

摘　　要：针对视网膜显微手术中的复杂干扰情况,本文利用深度学习的方法提出一种手术器械检测算法。首先,构建并手动标注了RET1数据集,并以YOLO框架为基础,针对部分图像退化,提出利用SGConv和RGSCSP特征提取模块增强模型对图像细节特征的提取能力。针对IoU损失函数收敛速度慢以及边界框回归不准确的问题,提出DeltaIoU边界框损失函数。最后,运用动态头部和解耦头部的集成对特征融合的目标进行检测。实验结果表明,提出的方法在RET1数据集上mAP50-95达到72.4%,相较原有算法提升了3.8%,并能在复杂手术场景中对器械有效检测,为后续手术显微镜自动跟踪以及智能化手术导航提供有效帮助。To address the challenges posed by complex interference in retinal microsurgery,this study presents a deep learning-based algorithm for surgical instrument detection.The RET1 dataset was first constructed and meticulously annotated to provide a reliable basis for training and evaluation.Building upon the YOLO framework,this study introduces the SGConv and RGSCSP feature extraction modules,specifically designed to enhance the model's capability to capture fine-grained image details,especially in scenarios involving degraded image quality.Furthermore,to address the issues of slow convergence in IoU loss and inaccuracies in bounding box regression,the DeltaIoU bounding box loss function was proposed to improve both detection precision and training efficiency.Additionally,the integration of dynamic and decoupled heads optimizes feature fusion,further enhancing the detection performance.Experimental results demonstrate that the proposed method achieves 72.4%mAP50-95 on the RET1 dataset,marking a 3.8%improvement over existing algorithms.The method also exhibits robust performance in detecting surgical instruments under various complex surgical scenarios,underscoring its potential to support automatic tracking in surgical microscopes and intelligent surgical navigation systems.

关 键 词：视网膜显微手术 目标检测 YOLO 手术显微镜 损失函数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]