Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model

Accurate and automatic colonic polyp segmentation and measurement in Computed Tomography (CT) has significant importance for 3D polyp detection, classification, and more generally computer aided diagnosis of colon cancers. In this paper, we propose a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from their surrounding tissues in CT. Our system integrates low-, and mid-level information for discriminative learning under local polar coordinates which align on the 3D colon surface around detected polyp. More importantly, our supervised learning system has flexible modeling capacity, which offers a principled means of encoding semantic, clinical expert annotations of colonic polyp tissue identification and segmentation. The learning generality to unseen data is bounded by boosting [12, 11] and stacked generality [14]. Extensive experimental results on polyp segmentation performance evaluation and robustness testing with disturbances (using both training data and unseen data) are provided to validate our presented approach. The reliability of polyp segmentation and measurement has been largely increased to 98.2% (ie. errors ≤ 3mm), compared with other state of art work [4, 15] of about 75%-80%.