Segmentation of Hematoxylin-Eosin stained breast cancer histopathological images based on pixel-wise SVM classifier

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• 作者：AiPing Qu ; JiaMei Chen ; LinWei Wang ; JingPing Yuan…
• 关键词：breast cancer histopathology images ; segmentation ; image analysis ; support vector machine ; survival analysis ; /li> ; ; /li> ; 092105
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：58
• 期：9
• 页码：1-13
• 全文大小：2,796 KB
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• 作者单位：AiPing Qu (1) (4)
  JiaMei Chen (2)
  LinWei Wang (2)
  JingPing Yuan (2)
  Fang Yang (2)
  QingMing Xiang (2)
  Ninu Maskey (3)
  GuiFang Yang (3)
  Juan Liu (1)
  Yan Li (2)
  
  1. Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China
  4. Center of High Performance Computing, Huaihua University, Huaihua, 418000, China
  2. Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, 430071, China
  3. Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

Hematoxylin-Eosin (HE) staining is the routine diagnostic method for breast cancer (BC), and large amounts of HE stained histopathological images are available for analysis. It is emergent to develop computational methods to efficiently and objectively analyze these images, with the aim of providing potentially better diagnostic and prognostic information for BC. This work focus on analyzing our in-house HE stained histopathological images of breast cancer tissues. Since tumor nests (TNs) and stroma morphological characteristics can reflect the biological behaviors of breast invasive ductal carcinoma (IDC), accurate segmentation of TNs and the stroma is the first step towards the subsequent quantitative analysis. We first propose a method based on the pixel-wise support vector machine (SVM) classifier for segmenting TNs and the stroma, then extract four morphological characters related to the TNs from the images and investigate their relationships with the patients-8-year disease free survival (8-DFS). The evaluation result shows that the classification based segmentation method is able to distinguish between TNs and stroma with 87.1% accuracy and 80.2% precision, suggesting that the proposed method is promising in segmenting HE stained IDC histopathological images. The Kaplan-Meier survival curves show that three morphological characters (number of TNs, total perimeter, and average area of TNs) in the images have statistical correlations with 8-DFS of the patients, illustrating that the segmented images can help to identify new morphological factors in IDC TNs for the prediction of BC prognosis.