A Level Set Method for Gland Segmentation

被引:5
|
作者
Wang, Chen [1 ]
Bu, Hong [2 ]
Bao, Ji [2 ]
Li, Chunming [1 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China
[2] Sichuan Univ, Chengdu, Sichuan, Peoples R China
来源
2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) | 2017年
关键词
D O I
10.1109/CVPRW.2017.120
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Histopathology plays a role as the gold standard in clinic for disease diagnosis. The identification and segmentation of histological structures are the prerequisite to disease diagnosis. With the advent of digital pathology, researchers' attention is attracted by the analysis of digital pathology images. In order to relieve the workload on pathologists, a robust segmentation method is needed in clinic for computer-assisted diagnosis. In this paper, we propose a level set framework to achieve gland image segmentation. The input image is divided into two parts, which contain glands with lumens and glands without lumens, respectively. Our experiments are performed on the clinical datasets of West China Hospital, Sichuan University. The experimental results show that our method can deal with glands without lumens, thus can obtain a better performance.
引用
收藏
页码:865 / 873
页数:9
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