Nuclear segmentation based on endocytoscopy system with high magnification

Wei ZHANG; Hao YU; Bo YUAN; Li-qiang WANG; Qing YANG

doi:10.37188/OPE.2021.0324

您当前的位置：

首页 >

文章列表页 >

Nuclear segmentation based on endocytoscopy system with high magnification

Modern Applied Optics | 更新时间：2022-01-25

- Nuclear segmentation based on endocytoscopy system with high magnification
- Optics and Precision Engineering Vol. 29, Issue 11, Pages: 2574-2580(2021)
- 作者机构：
  
  1.浙江大学光电科学与工程学院，浙江杭州 310027
  2.之江实验室超级感知研究中心，浙江杭州 311100
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.2021.0324
  CLC： R735.1;TP391.4
- Received：20 May 2021，
  
  Revised：04 June 2021，
  
  Published：15 November 2021
- 稿件说明：
移动端阅览
张伟,余浩,袁波等.基于高倍率细胞内镜系统的细胞核分割[J].光学精密工程,2021,29(11):2574-2580.

ZHANG Wei,YU Hao,YUAN Bo,et al.Nuclear segmentation based on endocytoscopy system with high magnification[J].Optics and Precision Engineering,2021,29(11):2574-2580.
张伟,余浩,袁波等.基于高倍率细胞内镜系统的细胞核分割[J].光学精密工程,2021,29(11):2574-2580. DOI： 10.37188/OPE.2021.0324.

ZHANG Wei,YU Hao,YUAN Bo,et al.Nuclear segmentation based on endocytoscopy system with high magnification[J].Optics and Precision Engineering,2021,29(11):2574-2580. DOI： 10.37188/OPE.2021.0324.

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0.813 8，为细胞内镜的AI辅助诊断算法研究奠定了基础。

Abstract

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 0.813 8， laying a foundation for the study of artificial intelligence-assisted diagnosis of endocytoscopy.

关键词

Keywords

references

CHEN W Q ， ZHENG R S ， BAADE P D ， et al . Cancer statistics in China， 2015 ［J］. CA： A Cancer Journal for Clinicians ， 2016 ， 66 （ 2 ）： 115 - 132 . doi: 10.3322/caac.21338 http://dx.doi.org/10.3322/caac.21338

KWON R S ， SONG L M WONGKEE ， ADLER D G ， et al . Endocytoscopy ［J］. Gastrointestinal Endoscopy ， 2009 ， 70 （ 4 ）： 610 - 613 . doi: 10.1016/j.gie.2009.06.030 http://dx.doi.org/10.1016/j.gie.2009.06.030

ABAD M R A ， SHIMAMURA Y ， FUJIYOSHI Y ， et al . Endocytoscopy： technology and clinical application in upper gastrointestinal tract ［J］. Translational Gastroenterology and Hepatology ， 2020 ， 5 ： 28 . doi: 10.21037/tgh.2019.11.12 http://dx.doi.org/10.21037/tgh.2019.11.12

陈亚丽，李赟，尹跃霏，等 . 细胞内镜在上消化道早癌中的应用研究［J］. 中华消化内镜杂志， 2018 ， 35 （ 10 ）： 773 - 776 . doi: 10.3760/cma.j.issn.1007-5232.2018.10.022 http://dx.doi.org/10.3760/cma.j.issn.1007-5232.2018.10.022

CHEN Y L ， LI Y ， YI Y F ， et al . Application of endocytoscopy in early cancer of upper gastrointestinal tract ［J］. Chinese Journal of Digestive Endoscopy ， 2018 ， 35 （ 10 ）： 773 - 776 . （in Chinese） . doi: 10.3760/cma.j.issn.1007-5232.2018.10.022 http://dx.doi.org/10.3760/cma.j.issn.1007-5232.2018.10.022

徐宝腾，杨西斌，刘家林，等 . 高速扫描激光共聚焦显微内窥镜图像校正［J］. 光学精密工程， 2020 ， 28 （ 1 ）： 60 - 68 . doi: 10.3788/ope.20202801.0060 http://dx.doi.org/10.3788/ope.20202801.0060

XU B T ， YANG X B ， LIU J L ， et al . Image correction for high speed scanning confocal laser endomicroscopy ［J］. Opt. Precision Eng. ， 2020 ， 28 （ 1 ）： 60 - 68 . （in Chinese） . doi: 10.3788/ope.20202801.0060 http://dx.doi.org/10.3788/ope.20202801.0060

KUMAGAI Y ， MONMA K ， KAWADA K . Magnifying chromoendoscopy of the esophagus： in-vivo pathological diagnosis using an endocytoscopy system ［J］. Endoscopy ， 2004 ， 36 （ 7 ）： 590 - 594 . doi: 10.1055/s-2004-814533 http://dx.doi.org/10.1055/s-2004-814533

KUMAGAI Y ， TAKUBO K ， KAWADA K ， et al . A newly developed continuous zoom-focus endocytoscope ［J］. Endoscopy ， 2017 ， 49 （ 2 ）： 176 - 180 . doi: 10.1055/s-0042-119267 http://dx.doi.org/10.1055/s-0042-119267

KUMAGAI Y ， TAKUBO K ， KAWADA K ， et al . Endocytoscopic observation of various types of esophagitis ［J］. Esophagus ， 2016 ， 13 （ 2 ）： 200 - 207 . doi: 10.1007/s10388-015-0517-1 http://dx.doi.org/10.1007/s10388-015-0517-1

ONO S ， NOZAKI A ， MATSUDA K ， et al . In vivo optical cellular diagnosis for uterine cervical or vaginal intraepithelial neoplasia using flexible gastrointestinal endocytoscopy-a prospective pilot study ［J］. BMC Cancer ， 2020 ， 20 （ 1 ）： 955 . doi: 10.1186/s12885-020-07460-6 http://dx.doi.org/10.1186/s12885-020-07460-6

KUMAGAI Y ， TAKUBO K ， KAWADA K ， et al . Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus ［J］. Esophagus ， 2019 ， 16 （ 2 ）： 180 - 187 . doi: 10.1007/s10388-018-0651-7 http://dx.doi.org/10.1007/s10388-018-0651-7

MOSHKOV N ， MATHE B ， KERTESZ-FARKAS A ， et al . Test-time augmentation for deep learning-based cell segmentation on microscopy images ［J］. Scientific Reports ， 2020 ， 10 （ 1 ）： 5068 . doi: 10.1038/s41598-020-61808-3 http://dx.doi.org/10.1038/s41598-020-61808-3

CAICEDO J C ， GOODMAN A ， KARHOHS K W ， et al . Publisher correction： Nucleus segmentation across imaging experiments： the 2018 Data Science Bowl ［J］. Nature Methods ， 2020 ， 17 （ 2 ）： 241 . doi: 10.1038/s41592-020-0733-z http://dx.doi.org/10.1038/s41592-020-0733-z

SHELHAMER E ， LONG J ， DARRELL T . Fully convolutional networks for semantic segmentation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2017 ， 39 （ 4 ）： 640 - 651 . doi: 10.1109/tpami.2016.2572683 http://dx.doi.org/10.1109/tpami.2016.2572683

SIMONYAN K ， ZISSERMAN A . Very deep convolutional networks for large-scale image recognition ［J］. 3rd International Conference on Learning Representations ， ICLR 2015 - Conference Track Proceedings， 2015 ： 1 - 14 . doi: 10.1109/iccv.2015.314 http://dx.doi.org/10.1109/iccv.2015.314

秦传波，宋子玉，曾军英，等 . 联合多尺度和注意力-残差的深度监督乳腺癌分割［J］. 光学精密工程， 2021 ， 29 （ 4 ）： 877 - 895 . doi: 10.37188/OPE.20212904.0877 http://dx.doi.org/10.37188/OPE.20212904.0877

QIN CH B ， SONG Z Y ， ZENG J Y ， et al . Deeply supervised breast cancer segmentation combined with multi-scale and attention-residuals ［J］. Opt. Precision Eng. ， 2021 ， 29 （ 4 ）： 877 - 895 . （in Chinese） . doi: 10.37188/OPE.20212904.0877 http://dx.doi.org/10.37188/OPE.20212904.0877

张文秀，朱振才，张永合，等 . 基于残差块和注意力机制的细胞图像分割方法［J］. 光学学报， 2020 ， 40 （ 17 ）： 76 - 83 . doi: 10.3788/aos202040.1710001 http://dx.doi.org/10.3788/aos202040.1710001

ZHANG W X ， ZHU ZH C ， ZHANG Y H ， et al . Cell image segmentation method based on residual block and attention mechanism ［J］. Acta Optica Sinica ， 2020 ， 40 （ 17 ）： 76 - 83 . （in Chinese） . doi: 10.3788/aos202040.1710001 http://dx.doi.org/10.3788/aos202040.1710001

KINGMA D ， BA J . Adam： a method for stochastic optimization ［EB/OL］. https://arxiv.org/abs/1412.6980. 2014 https://arxiv.org/abs/1412.6980.2014 .

KUMAGAI Y ， KAWADA K ， HIGASHI M ， et al . Endocytoscopic observation of various esophageal lesions at × 600： can nuclear abnormality be recognized？［J］. Diseases of the Esophagus ， 2015 ， 28 （ 3 ）： 269 - 275 . doi: 10.1111/dote.12183 http://dx.doi.org/10.1111/dote.12183

Views

470

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Underwater image enhancement based on multi-branch residual attention network

Cross-modality image matching algorithm based on policy gradient and pseudo-twin network

Shape adaptive feature aggregation network for point cloud classification and segmentation

Real-time super-resolution for infrared dynamic object video based on airborne platform

Related Author

Hao YU

Bo YUAN

Li-qiang WANG

Qing YANG

CHENG Zhuming

LI Jiaxuan

HUANG San'ao

HAN Lichao

Related Institution

School of Electrical and Information Engineering， Anhui University of Technology， Maanshan

Key Laboratory of Opto-Electronic Information Processing，Chinese Academy of Sciences

Shenyang Institute of Automation， Chinese Academy of Sciences

University of Chinese Academy of Sciences

Key Laboratory of Optoelectronic Technology and System， Ministry of Education， Chongqing University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰