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

Kenji Suzuki | Zendy; Ivan Sheu | Zendy; Don C. Rockey | Zendy; Abraham H. Dachman | Zendy

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

Author(s) -

Kenji Suzuki,

Ivan Sheu,

Don C. Rockey,

Abraham H. Dachman

Publication year - 2009

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.811073

Subject(s) - cad , computer science , computer aided diagnosis , artificial intelligence , linear discriminant analysis , segmentation , virtual colonoscopy , radiology , pattern recognition (psychology) , artificial neural network , computer vision , medicine , colonoscopy , colorectal cancer , cancer , engineering drawing , engineering

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.

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