A CAD utilizing 3D massive-training ANNs for detection of flat lesions in CT colonography: preliminary results

Kenji Suzuki; Ivan Sheu; Don C. Rockey M.D.; Abraham H. Dachman M.D.

doi:10.1117/12.811073

27 February 2009 A CAD utilizing 3D massive-training ANNs for detection of flat lesions in CT colonography: preliminary results

Kenji Suzuki, Ivan Sheu, Don C. Rockey M.D., Abraham H. Dachman M.D.

Author Affiliations +

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601A (2009) https://doi.org/10.1117/12.811073
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Our purpose was to develop a computer-aided diagnostic (CAD) scheme for detection of flat lesions (also known as superficial elevated or depressed lesions) in CT colonography (CTC), which utilized 3D massive-training artificial neural networks (MTANNs) for false-positive (FP) reduction. Our CAD scheme consisted of colon segmentation, polyp candidate detection, linear discriminant analysis, and MTANNs. To detect flat lesions, we developed a precise shape analysis in the polyp detection step to accommodate the analysis to include a flat shape. With our MTANN CAD scheme, 68% (19/28) of flat lesions, including six lesions "missed" by radiologists in a multicenter clinical trial, were detected correctly, with 10 (249/25) FPs per patient.

Citation Download Citation

Kenji Suzuki, Ivan Sheu, Don C. Rockey M.D., and Abraham H. Dachman M.D. "A CAD utilizing 3D massive-training ANNs for detection of flat lesions in CT colonography: preliminary results", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601A (27 February 2009); https://doi.org/10.1117/12.811073

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