Ensemble detection of colorectal lesions for CT colonography

Janne J. Näppi, Daniele Regge, Hiroyuki Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages60-67
Number of pages8
Volume7029 LNCS
DOIs
Publication statusPublished - 2012
Event3rd 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 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7029 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd 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
CountryCanada
CityToronto, ON
Period9/18/119/18/11

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Keywords

  • computer-aided detection
  • ensemble detection
  • machine learning
  • polyp detection
  • virtual colonoscopy

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Näppi, J. J., Regge, D., & Yoshida, H. (2012). Ensemble detection of colorectal lesions for CT colonography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7029 LNCS, pp. 60-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7029 LNCS). https://doi.org/10.1007/978-3-642-28557-8_8