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.
This work was supported in part by NSF grants CCF-0830402 and CCF-0844765, and NIH grants R01-EB004640 and K25-CA123112.
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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: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_6
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DOI: https://doi.org/10.1007/978-3-642-22092-0_6
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