[1]朱世祺,徐昶,周鑫,等.基于DeepLab V3+深度神经网络的结直肠息肉内镜图像分割[J].中国医学物理学杂志,2023,40(8):944-949.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.004]
　ZHU Shiqi,XU Chang,et al.Colorectal polyp segmentation in endoscopic images using DeepLab V3+[J].Chinese Journal of Medical Physics,2023,40(8):944-949.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.004]
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基于DeepLab V3+深度神经网络的结直肠息肉内镜图像分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第8期
页码:
944-949
栏目:
医学影像物理
出版日期:
2023-09-01

文章信息/Info

Title:
Colorectal polyp segmentation in endoscopic images using DeepLab V3+
文章编号:
1005-202X（2023）08-0944-06
作者:
朱世祺1; 2; 徐昶1; 2; 周鑫3; 刘璐1; 2; 林嘉希1; 2; 殷民月1; 2; 刘晓琳1; 2; 许春芳1; 2; 朱锦舟1; 2
1.苏州大学附属第一医院消化内科， 江苏 苏州 215006； 2.苏州市消化病临床医学中心， 江苏 苏州 215006； 3.江苏大学附属金坛医院消化内科， 江苏 常州 213200
Author(s):
ZHU Shiqi1;  2;  XU Chang1;  2;  ZHOU Xin3;  LIU Lu1;  2;  LIN Jiaxi1;  2;  YIN Minyue1;  2;  LIU Xiaolin1;  2;  XU Chunfang1;  2;  ZHU Jinzhou1;  2
1. Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou 215006, China 2. Suzhou Clinical Centre of Digestive Disease, Suzhou 215006, China 3.Department of Gastroenterology, Jintan Affiliated Hospital of Jiangsu University, Changzhou 213200, China
关键词:
语义分割; 深度学习; DeepLab V3+; 结直肠息肉
Keywords:
Keywords: semantic segmentation deep learning DeepLab V3+ colorectal polyp
分类号:
R318；R574
DOI:
DOI:10.3969/j.issn.1005-202X.2023.08.004
文献标志码:
A
摘要:
目的：基于深度神经网络DeepLab V3+建立结直肠息肉内镜图像语义分割模型。方法：选取Hyper-Kvasir数据库1 000张、苏州大学附属第一医院500张结直肠息肉内镜图像，分为训练集（n=1 200）和验证集（n=300），同时收集江苏大学附属金坛医院肠息肉图像作为测试集（n=220）。对内镜图像进行分割标记，载入以DeepLab V3+为框架的深度神经网络中训练，建立语义分割模型。结果：在内部验证集中，该模型的准确性（ACC）达97.2%，平均交并比（MIoU）达85.8%，Dice系数达0.924。在外部测试集中，ACC达98.0%，MIoU达80.1%，Dice系数达0.890。结论：基于DeepLab V3+深度神经网络，构建结直肠息肉内镜图像的语义分割模型，具有良好的预测性能，可作为检测结直肠息肉的有效工具。
Abstract:
Abstract: Objective To establish a semantic segmentation model for colorectal polyps in endoscopic images based on DeepLab V3+. Methods A total of 1 500 endoscopic images of colorectal polyps were collected, including 1 000 from Hyper-Kvasir public dataset and 500 from the First Affiliated Hospital of Soochow University, and randomly divided into training set (n=1 200) and validation set (n=300). Meanwhile, the images from Jintan Affiliated Hospital of Jiangsu University were collected as test set (n=220). After the endoscopic image segmentation and labeling, the images and masks were loaded into a deep learning neural network with DeepLab V3+ as the architecture for training, thereby developing a semantic segmentation model. Results The accuracy, mean intersection over union, and Dice coefficient of the developed model were 97.2%, 85.8% and 0.924 in the internal validation set, and 98.0%, 80.1% and 0.890 in the external test set. Conclusion The DeepLab V3+ based semantic segmentation model for colorectal polyps in endoscopic images exhibits excellent performances, and it can serve as an effective method for the detection and diagnosis of colorectal polyps.

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备注/Memo

备注/Memo:
【收稿日期】2023-02-18 【基金项目】国家自然科学基金（82000540）；苏州市科教兴卫青年项目（KJXW2019001）；苏州大学医学部学生课外科研项目（2021YXBKWKY050）；苏州市科技计划（SKY2021038）；苏州市消化病临床医学中心（Szlcyxzx202101） 【作者简介】朱世祺，硕士，研究方向：人工智能在消化病中的应用，E-mail: 1830805021@stu.suda.edu.cn 【通信作者】朱锦舟，博士，副主任医师，研究方向：人工智能在消化病中的应用，E-mail: jzzhu@zju.edu.cn
更新日期/Last Update: 2023-09-06