Automated liver segmentation from a postmortem CT scan based on a statistical shape model

作     者：Saito, Atsushi Yamamoto, Seiji Nawano, Shigeru Shimizu, Akinobu 

作者机构：Tokyo Univ Agr & Technol Koganei Tokyo Japan Ai Informat Ctr Chuo Ku Tokyo Japan Int Univ Hlth & Welf Mita Hosp Dept Radiol Minato Ku Tokyo Japan 

出 版 物：《INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY》 (国际计算机辅助放射学与外科学杂志)

年 卷 期：2017年第12卷第2期

页      面：205-221页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1006[医学-中西医结合] 1002[医学-临床医学] 1009[医学-特种医学] 10[医学] 100602[医学-中西医结合临床] 

基　　金：JSPS KAKENHI Grant [15J08775] MEXT KAKENHI Grant Grants-in-Aid for Scientific Research [16H06785, 15K21716, 15J08775, 26108001, 26108002] Funding Source: KAKEN 

主　　题：Liver segmentation Postmortem CT Statistical shape model EM algorithm Autopsy imaging Synthesized-based learning 

摘      要：Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver. The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label. The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference. We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.