Automatic multiorgan segmentation in CT images of the male pelvis using region‐specific hierarchical appearance cluster models. Issue 10 (13th September 2016)

• Record Type: Journal Article
• Authors: Li, Dengwang; Zang, Pengxiao; Chai, Xiangfei; Cui, Yi; Li, Ruijiang; Xing, Lei
• Is Part Of: Medical physics. Volume 43:Issue 10(2016)
• Page Start: 5426
• Page End: 5436
• Publication Date: 2016-09-13
• Subjects: biological organs -- cancer -- computerised tomography -- image segmentation -- medical image processing -- principal component analysis -- radiation therapy; Computed tomography -- Probability theory, stochastic processes, and statistics -- Therapeutic applications, including brachytherapy; Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; automatic multiorgan segmentation -- radiotherapy treatment planning -- image segmentation -- cancer treatment; Computed tomography -- Anatomy -- Medical image segmentation -- Cancer -- Nonlinear acoustics -- Normal modes -- Biomedical modeling -- Radiation therapy -- Cluster analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4962468
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis. Methods: The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region‐specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA‐based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method. Results: In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM. Conclusions: The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.