Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume

This paper presents a novel graph cut algorithm that can take into account multi-shape constraints with neighbor prior constraints, and reports on a lung segmentation process from a three-dimensional computed tomography (CT) image based on this algorithm. The major contribution of this paper is the proposal of a novel segmentation algorithm that improves lung segmentation for cases in which the lung has a unique shape and pathologies such as pleural effusion by incorporating multiple shapes and prior information on neighbor structures in a graph cut framework. We demonstrate the efficacy of the proposed algorithm by comparing it to conventional one using a synthetic image and clinical thoracic CT volumes.

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► We propose a graph cut algorithm that can take into account the multiple shapes.
► We propose novel energy terms to introduce priors on neighboring structures.
► We performed experiments using a synthetic image and 97 clinical CT volumes.
► The multi-shape graph cuts with all neighbor constraints and adaptive weight gave the best performance.