摘要
临床上诊断消化道早期癌症主要依赖于电子内镜活检术,但是其诊断周期长。细胞内镜是一种具有超高放大倍率的内窥镜,配合术中染色可以直接在体内观察到病灶的细胞核等病理结构。为了使内窥镜医生能够在术中更准确地分析细胞核病理特征,基于已研制的高倍率细胞内镜系统在猪食管黏膜组织上开展了细胞核染色及分割方法研究。利用1%浓度的甲苯胺蓝水溶液对猪食管黏膜进行细胞核染色,并成功在细胞内镜显微成像模式下观察到染色的细胞核。在此基础上,采用深度学习方法训练了细胞核分割模型,有效实现了染色细胞核的分割提取,分割准确度达到了99.23%,特异性达到了99.54%,敏感性达到了84.37%,Dice系数达到了0.8138,为细胞内镜的AI辅助诊断算法研究奠定了基础。
Endoscopic biopsy is the main approach to the clinical diagnosis of early gastrointestinal cancer to date.However,this approach warrants a long period to obtain the final diagnosis.Endocytoscopy is a type of endoscope with ultra-high magnification,which,combined with intraoperative staining,can directly observe the pathological structure of the lesion such as the nucleus in vivo.To make endoscopists more accurately analyze the pathological features of the nucleus during the operation,a nuclear staining and segmentation method was previously developed for the esophageal mucosa tissue of pigs based on the endocytoscopy system with high magnification.Firstly,1%toluidine blue was used to stain the nucleus of esophageal mucosa tissue,and the stained nuclei were observed successfully under the microscopic imaging mode of endocytoscopy.Based on this,the deep learning method was adopted to train the nuclear segmentation model,which effectively realized the segmentation and extraction of stained nuclei.The pixel accuracy reaches 99.23%,specificity of 99.54%,sensitivity of 84.37%,and the Dice of 0.8138,laying a foundation for the study of artificial intelligence-assisted diagnosis of endocytoscopy.
作者
张伟
余浩
袁波
王立强
杨青
ZHANG Wei;YU Hao;YUAN Bo;WANG Li-qiang;YANG Qing(College of Optical Science and Engineering,Zhejiang University,Hangzhou 310027,China;Research Center for Intelligent Sensing,Zhejiang Lab,Hangzhou 311100,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2021年第11期2574-2580,共7页
Optics and Precision Engineering
基金
国家重点研发计划资助项目(No.2019YFC0119502)
浙江省重点研发计划资助项目(No.2018C03064)
中央高校基本科研业务费专项(No.2019FZA5016)。
