title: Haustral fold segmentation with curvature-guided level set evolution.
abstract: Human colon has complex structures mostly because of the haustral folds. The folds are thin flat protrusions on the colon wall, which complicate the shape analysis for computer-aided detection (CAD) of colonic polyps. Fold segmentation may help reduce the structural complexity, and the folds can serve as an anatomic reference for computed tomographic colonography (CTC). Therefore, in this study, based on a model of the haustral fold boundaries, we developed a level-set approach to automatically segment the fold surfaces. To evaluate the developed fold segmentation algorithm, we first established the ground truth of haustral fold boundaries by experts' drawing on 15 patient CTC datasets without severe under/over colon distention from two medical centers. The segmentation algorithm successfully detected 92.7% of the folds in the ground truth. In addition to the sensitivity measure, we further developed a merit of segmented-area ratio (SAR), i.e., the ratio between the area of the intersection and union of the expert-drawn folds and the area of the automatically segmented folds, to measure the segmentation accuracy. The segmentation algorithm reached an average value of SAR = 86.2%, showing a good match with the ground truth on the fold surfaces. We believe the automatically segmented fold surfaces have the potential to benefit many postprocedures in CTC, such as CAD, taenia coli extraction, supine-prone registration, etc.
authors.affiliation: Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA. hbzhu@mil.sunysb.edu
authors.forename: Hongbin
authors.initials: H
authors.lastname: Zhu
authors.forename: Matthew
authors.initials: M
authors.lastname: Barish
authors.forename: Perry
authors.initials: P
authors.lastname: Pickhardt
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D003106:Colon / Q000033:anatomy & histology*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*