TY - GEN
T1 - Ensemble detection of colorectal lesions for CT colonography
AU - Näppi, Janne J.
AU - Regge, Daniele
AU - Yoshida, Hiroyuki
PY - 2012
Y1 - 2012
N2 - Even though different computer-aided detection (CAD) systems for computed tomographic colonography (CTC) have similar overall detection accuracies, they are known to detect different types of lesions and false positives. We implemented an ensemble CAD scheme for merging the detection results of different CAD systems in CTC. After normalizing of the lesion-likelihood data between different systems, a Bayesian classifier was used for determining the final detections. For evaluation, we collected 218 abnormal patients with 263 lesions ≥6 mm. The detection accuracies of three CAD systems were compared with that of their ensemble CAD scheme by use of independent training and testing. The preliminary results indicate that the ensemble CAD scheme can yield a higher overall detection accuracy than can individual CAD systems. In particular, the ensemble scheme was able to detect flat lesions at high sensitivity without compromising a high polyp detection accuracy.
AB - Even though different computer-aided detection (CAD) systems for computed tomographic colonography (CTC) have similar overall detection accuracies, they are known to detect different types of lesions and false positives. We implemented an ensemble CAD scheme for merging the detection results of different CAD systems in CTC. After normalizing of the lesion-likelihood data between different systems, a Bayesian classifier was used for determining the final detections. For evaluation, we collected 218 abnormal patients with 263 lesions ≥6 mm. The detection accuracies of three CAD systems were compared with that of their ensemble CAD scheme by use of independent training and testing. The preliminary results indicate that the ensemble CAD scheme can yield a higher overall detection accuracy than can individual CAD systems. In particular, the ensemble scheme was able to detect flat lesions at high sensitivity without compromising a high polyp detection accuracy.
KW - computer-aided detection
KW - ensemble detection
KW - machine learning
KW - polyp detection
KW - virtual colonoscopy
UR - http://www.scopus.com/inward/record.url?scp=84858321935&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-28557-8_8
DO - 10.1007/978-3-642-28557-8_8
M3 - Conference contribution
AN - SCOPUS:84858321935
SN - 9783642285561
VL - 7029 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 60
EP - 67
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 3rd International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 18 September 2011
ER -