Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approach.
Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained.
Medical Imaging Department, Taizhou Municipal Hospital, No.381 East Zhongshan Road, Taizhou 318000, China.
Wenjun
W
Cheng
Medical Imaging Department, Taizhou Municipal Hospital, No.381 East Zhongshan Road, Taizhou 318000, China.
Luyao
L
Ma
Medical Imaging Department, Taizhou Municipal Hospital, No.381 East Zhongshan Road, Taizhou 318000, China.
Tiejun
T
Yang
College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou 310018, China.
Jiali
J
Liang
College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou 310018, China.
Yan
http://orcid.org/0000-0002-2332-4230
Y
Zhang
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008962:Models, Theoretical; D014057:Tomography, X-Ray Computed