基于微变放大技术的肺结节识别算法研究  

Research on pulmonary nodule recognition algorithm based on micro-variation amplification

作　　者：张子睿 焦子宸 史校铭 王涛 ZHANG Zirui;JIAO Zichen;SHI Xiaoming;WANG Tao(Department of Thoracic Surgery,Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University,Nanjing,210008,P.R.China;Department of Thoracic Surgery,The Affiliated Drum Tower Hospital of Nanjing University Medical School,Nanjing,210008,P.R.China)

机构地区：[1]南京医科大学鼓楼临床医学院胸外科,南京210008 [2]南京大学医学院附属鼓楼医院胸外科,南京210008

出　　处：《中国胸心血管外科临床杂志》2025年第3期339-344,共6页Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

基　　金：2023年度南京市卫生科技发展专项资金项目(ZKX23015);2022年度生命健康科技专项资金项目(202205046)。

摘　　要：目的开发一种能够帮助医师定位肺结节的新型识别算法。方法纳入2023年12月于南京大学医学院附属鼓楼医院胸外科行胸腔镜手术的肺结节患者。采集患者60帧/s帧率、1920×1080分辨率的胸腔镜肺表面探查数据,并以定间隔保存帧图像,对帧进行分块处理。用以上数据构建一个针对肺部结节识别的算法数据库。结果共纳入16例患者,其中男9例、女7例,平均年龄(54.9±14.9)岁。在经过优化的多拓扑卷积网络模型中,测试结果显示准确识别率达94.39%。进一步通过整合微变放大技术的卷积网络模型,在识别肺部结节方面的准确率提高至96.90%。将这2个模型的表现综合评估,整体的识别准确率达95.59%。据此,我们推断所提出的网络模型适用于肺部结节的识别任务,且融合微变放大技术的卷积网络在准确率上表现更为优异。结论我们提出的技术模型能够显著提高肺部结节识别的精度和定位准确度并帮助术者在胸腔镜手术过程中定位肺结节。Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules.Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery,Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023,were enrolled in the study.Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1920×1080.Frame images were saved at regular intervals for subsequent block processing.An algorithm database for lung nodule recognition was developed using the collected data.Results A total of 16 patients were enrolled,including 9 males and 7 females,with an average age of(54.9±14.9)years.In the optimized multi-topology convolutional network model,the test results demonstrated an accuracy rate of 94.39%for recognition tasks.Furthermore,the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%.A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%.Based on these findings,we conclude that the proposed network model is wellsuited for the task of lung nodule recognition,with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy.Conclusion Compared to traditional methods,our proposed technique significantly enhances the accuracy of lung nodule identification and localization,aiding surgeons in locating lung nodules during thoracoscopic surgery.

关 键 词：肺结节 术前定位 微变放大 计算机视觉 多尺度检测 

分 类 号：R73[医药卫生—肿瘤]