Robust large scale prone-supine polyp matching using local features: a metric learning approach.

Computer aided detection (CAD) systems have emerged as noninvasive and effective tools, using 3D CT Colonography (CTC) for early detection of colonic polyps. In this paper, we propose a robust and automatic polyp prone-supine view matching method, to facilitate the regular CTC workflow where radiologists need to manually match the CAD findings in prone and supine CT scans for validation. Apart from previous colon registration approaches based on global geometric information, this paper presents a feature selection and metric distance learning approach to build a pairwise matching function (where true pairs of polyp detections have smaller distances than false pairs), learned using local polyp classification features. Thus our process can seamlessly handle collapsed colon segments or other severe structural artifacts which often exist in CTC, since only local features are used, whereas other global geometry dependent methods may become invalid for collapsed segmentation cases. Our automatic approach is extensively evaluated using a large multi-site dataset of 195 patient cases in training and 223 cases for testing. No external examination on the correctness of colon segmentation topology is needed. The results show that we achieve significantly superior matching accuracy than previous methods, on at least one order-of-magnitude larger CTC datasets.