title: Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.
abstract: Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.
authors.affiliation: Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA 52242, USA. qi-song@uiowa.edu
authors.forename: Qi
authors.initials: Q
authors.lastname: Song
authors.forename: Mingqing
authors.initials: M
authors.lastname: Chen
authors.forename: Junjie
authors.initials: J
authors.lastname: Bai
authors.forename: Milan
authors.initials: M
authors.lastname: Sonka
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*