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. © 2011 Springer-Verlag.
CITATION STYLE
Song, Q., Chen, M., Bai, J., Sonka, M., & Wu, X. (2011). Surface-region context in optimal multi-object graph-based segmentation: Robust delineation of pulmonary tumors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6801 LNCS, pp. 61–72). https://doi.org/10.1007/978-3-642-22092-0_6
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