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Proceedings Paper

Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall
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Paper Abstract

Further improvement of computer-aided detection (CADe) of colonic polyps is vital to advance computed tomographic colonography (CTC) toward a screening modality, where the detection of flat polyps is especially challenging because limited image features can be extracted from flat polyps, and the traditional geometric features-based CADe methods usually fail to detect such polyps. In this paper, we present a novel pipeline to automatically detect initial polyp candidates (IPCs), especially flat polyps, from CTC images. First, the colon wall mucosa was extracted via a partial volume segmentation approach as a volumetric layer, where the inner border of colon wall can be obtained by shrinking the volumetric layer using level set based adaptive convolution. Then the outer border of colon wall (or the colon wall serosa) was segmented via a combined implementation of geodesic active contour and Mumford-Shah functional in a coarse-to-fine manner. Finally, the wall thickness was estimated along a unique path between the segmented inner and outer borders with consideration of the volumetric layers and was mapped onto a patient-specific three-dimensional (3D) colon wall model. The IPC detection results can usually be better visualized in a 2D image flattened from the 3D model, where abnormalities were detected by Z-score transformation of the thickness values. The proposed IPC detection approach was validated on 11 patients with 22 CTC scans, and each scan has at least one flat poly annotation. The above presented novel pipeline was effective to detect some flat polyps that were missed by our CADe system while keeping false detections in a relative low level. This preliminary study indicates that the presented pipeline can be incorporated into an existing CADe system to enhance the polyp detection power, especially for flat polyps.

Paper Details

Date Published: 20 March 2015
PDF: 10 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141F (20 March 2015); doi: 10.1117/12.2082415
Show Author Affiliations
Hao Han, Stony Brook Univ. (United States)
Lihong Li, College of Staten Island, CUNY (United States)
Chaijie Duan, Tsinghua Univ. (United States)
Yang Zhao, Stony Brook Univ. (United States)
Huafeng Wang, Beihang Univ. (China)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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