首页 > 论文 > 激光与光电子学进展 > 57卷 > 20期(pp:201701--1)

嗜酸性粒细胞胃肠炎病理切片的计算机辅助诊断

Computer-Aided Diagnosis of Pathological Section for Eosinophilic Gastroenteritis

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

嗜酸性粒细胞胃肠炎(EG)是一种以外周血嗜酸性粒细胞(EOS)增多为特征的胃肠道疾病,其主要诊断依据为消化道黏膜标本病理切片中嗜酸性粒细胞的数目是否超标。利用计算机图像分析算法对病理切片图像中的嗜酸性粒细胞进行识别并计数,旨在辅助病理医生人工计算EOS的数目,减少医生的工作量,提高工作效率。采用鲁棒性较强的分水岭算法作为识别EOS的核心算法,并通过距离变换和前后景标记的改进算法解决传统分水岭算法中的过分割问题,提高识别计数的准确性。采用改进分水岭算法对EG病理图像中的EOS进行识别计数,并将其与病理医生的金标准进行比对。改进分水岭算法的平均准确率为95.0%。与传统算法相比,改进算法准确率的相对标准方差由5.8%提高到2.2%,过分割率由13.4%降低为3.7%,算法的运行时间由40 s缩短为27 s左右。

Abstract

Eosinophilic gastroenteritis (EG) is a gastrointestinal disease characterized by an increase in peripheral blood eosinophil (EOS). The main diagnosis of EG is based on whether the number of eosinophils in the pathological section of a digestive tract mucosa specimen exceeds the standard. In this study, a computer image analysis algorithm was used to identify and count eosinophils in pathological section images, with the aim to assist pathologists to manually calculate the number of EOS and reduce the workload and improve the work efficiency of doctors. A robust watershed algorithm was used as the core algorithm for identifying EOS, and the over-segmentation problem in the traditional watershed algorithm was solved using an improved distance transform algorithm and foreground and background markers. The accuracy of the watershed algorithm for recognition and counting was improved. The improved watershed algorithm was used to identify and count EOS, and its results were compared with a pathologist''s standard. The average accuracy of the algorithm is 95.0%. Compared with the traditional watershed algorithm, the relative standard deviation of the improved algorithm improved from 5.8% to 2.2%, the over-segmentation rate reduced from 13.4% to 3.7%, and the running time of the algorithm reduced from 40 s to about 27 s.

广告组1 - 空间光调制器+DMD
补充资料

中图分类号:R573

DOI:10.3788/LOP57.201701

所属栏目:医用光学与生物技术

基金项目:河北省硕士研究生创新资助项目;

收稿日期:2020-01-06

修改稿日期:2020-02-10

网络出版日期:2020-10-01

作者单位    点击查看

万真真:河北大学电子信息工程学院, 河北 保定 071002
李春雪:河北大学电子信息工程学院, 河北 保定 071002
刘芳:保定市儿童医院病理科, 河北 保定 071000保定市儿童呼吸消化疾病临床研究重点实验室, 河北 保定 071000
张绍永:河北大学电子信息工程学院, 河北 保定 071002
韩帅:河北大学电子信息工程学院, 河北 保定 071002

联系人作者:万真真(wanzhenzhen@126.com)

备注:河北省硕士研究生创新资助项目;

【1】Chen F. Research on the typing and parallelization of pathological tissue section images [D]. Guiyang: Guizhou University. 2019, 1-7.
陈凡. 病理组织切片图像的分型识别及其并行化研究 [D]. 贵阳: 贵州大学. 2019, 1-7.

【2】Soille P J, Ansoult M M. Automated basin delineation from digital elevation models using mathematical morphology [J]. Signal Processing. 1990, 20(2): 171-182.Soille P J, Ansoult M M. Automated basin delineation from digital elevation models using mathematical morphology [J]. Signal Processing. 1990, 20(2): 171-182.

【3】González-Betancourt A, Rodríguez-Ribalta P, Meneses-Marcel A, et al. Automated marker identification using the Radon transform for watershed segmentation [J]. IET Image Processing. 2017, 11(3): 183-189.

【4】Koyuncu C F, Akhan E, Ersahin T, et al. Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation [J]. Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2016, 89(4): 338-349.

【5】Guo J J, Xu M, Kong L A. People counting algorithm based on improved HOG features Information Technology and Informatization[J]. 0, 2019(3): 81-84.
郭晶晶, 许萌, 孔令爱. 基于改进HOG特征的人数统计算法 信息技术与信息化[J]. 0, 2019(3): 81-84.

【6】Riggle K M, Wahbeh G, Williams E M, et al. Perforated duodenal ulcer: an unusual manifestation of allergic eosinophilic gastroenteritis [J]. World Journal of Gastroenterology. 2015, 21(44): 12709.

【7】Zhao Q H, Wang Y H, Gao X Y, et al. Filtering evaluation method of phase images based on smooth spline fitting [J]. Acta Optica Sinica. 2018, 38(8): 0815020.
赵琪涵, 王永红, 高新亚, 等. 基于平滑样条拟合的相位图像滤波评价方法 [J]. 光学学报. 2018, 38(8): 0815020.

【8】Zhang J G, Feng W Z, Hu C H, et al. Image segmentation method for forestry unmanned aerial vehicle pest monitoring based on composite gradient watershed algorithm [J]. Transactions of the Chinese Society of Agricultural Engineering. 2017, 33(14): 93-99.
张军国, 冯文钊, 胡春鹤, 等. 无人机航拍林业虫害图像分割复合梯度分水岭算法 [J]. 农业工程学报. 2017, 33(14): 93-99.

【9】Gush T. Bukhari S B A, Haider R, et al. Fault detection and location in a microgrid using mathematical morphology and recursive least square methods [J]. International Journal of Electrical Power & Energy Systems. 2018, 102: 324-331.

【10】Yu W H. A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks [J]. Applied Soft Computing. 2019, 85: 105785.

【11】Stringhini R M, Welfer D, Dornellas M C, et al. A mathematical morphology-based filter for noise reduction and detail preservation in low-dose dental CT images [J]. Studies in health technology and informatics. 2019, 264: 253-257.

【12】Das J K, Choudhury P P, Chaturvedi N, et al. Ranking and clustering of Drosophila olfactory receptors using mathematical morphology [J]. Genomics. 2019, 111(4): 549-559.

【13】Shirazi S H, Umar A I, Naz S, et al. Efficient leukocyte segmentation and recognition in peripheral blood image [J]. Technology and Health Care. 2016, 24(3): 335-347.

【14】Anter A M, Hassenian A E. CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm [J]. Artificial Intelligence in Medicine. 2019, 97: 105-117.

【15】Gamarra M, Zurek E, Escalante H J, et al. Split and merge watershed: a two-step method for cell segmentation in fluorescence microscopy images [J]. Biomedical Signal Processing and Control. 2019, 53: 101575.

【16】Pang C Y, Liu J K. Improved LFP algorithm on leukocyte image texture feature extraction and recognition [J]. Acta Photonica Sinica. 2013, 42(11): 1375-1380.
庞春颖, 刘记奎. 改进的LFP算法在白细胞图像纹理特征提取与识别中的应用 [J]. 光子学报. 2013, 42(11): 1375-1380.

【17】Hou H, Shi Y X. Application of the improved watershed algorithm based on distance transform in white blood cell segmentation [J]. Computing Technology and Automation. 2016, 35(3): 81-84.
侯慧, 石跃祥. 基于距离变换的改进分水岭算法在白细胞图像分割中的应用 [J]. 计算技术与自动化. 2016, 35(3): 81-84.

引用该论文

Wan Zhenzhen,Li Chunxue,Liu Fang,Zhang Shaoyong,Han Shuai. Computer-Aided Diagnosis of Pathological Section for Eosinophilic Gastroenteritis[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201701

万真真,李春雪,刘芳,张绍永,韩帅. 嗜酸性粒细胞胃肠炎病理切片的计算机辅助诊断[J]. 激光与光电子学进展, 2020, 57(20): 201701

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF