Accurate liver vessel segmentation via active contour model with dense vessel candidates. (November 2018)

• Record Type: Journal Article
• Authors: Chung, Minyoung; Lee, Jeongjin; Chung, Jin Wook; Shin, Yeong-Gil
• Is Part Of: Computer methods and programs in biomedicine. Volume 166(2018)
• Page Start: 61
• Page End: 75
• Publication Date: 2018-11
• Subjects: Active contour -- Block matching 3D (BM3D) -- Level set -- Liver -- Maximum intensity -- Segmentation -- Vessel; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.010
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new fully automated liver vessel segmentation algorithm is proposed. Our method can generate a smooth and accurate boundary of the vessel object. Our method can easily extract thin and weak peripheral branch vessels. Accuracies are quantified by new measures using structural branching points. Abstract: Background and objective: The purpose of this paper is to propose a fully automated liver vessel segmentation algorithm including portal vein and hepatic vein on contrast enhanced CTA images. Methods: First, points of a vessel candidate region are extracted from 3-dimensional (3D) CTA image. To generate accurate points, we reduce 3D segmentation problem to 2D problem by generating multiple maximum intensity (MI) images. After the segmentation of MI images, we back-project pixels to the original 3D domain. We call these voxels as vessel candidates (VCs). A large set of MI images can produce very dense and accurate VCs. Finally, for the accurate segmentation of a vessel region, we propose a newly designed active contour model (ACM) that uses the original image, vessel probability map from dense VCs, and the good prior of an initial contour. Results: We used 55 abdominal CTAs for a parameter study and a quantitative evaluation. We evaluated the performance of the proposed method comparing with other state-of-the-art ACMs for vascular images applied directly to the original data. The result showed that our method successfully segmented vascular structure 25%-122% more accurately than other methods without any extra false positive detection. Conclusion: Our model can generate a smooth and accurate boundary of the vessel object and easily extract thin and weak peripheral branch vessels. The proposed approach can automatically segment a liver vessel without any manual interaction. The detailed result can aid further anatomical studies.