Computer-aided detection of polyps in CT colonography with pixel-based machine learning techniques

  • Authors:

  • Jian-Wu Xu;Kenji Suzuki

  • Affiliations:

  • Department of Radiology, The University of Chicago, Chicago, IL;Department of Radiology, The University of Chicago, Chicago, IL

  • Venue:
  • MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
  • Year:
  • 2011

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Abstract

Pixel/voxel-based machine-learning techniques have been developed for classification between polyp regions of interest (ROIs) and non-polyp ROIs in computer-aided detection (CADe) of polyps in CT colonography (CTC). Although 2D/3D ROIs can be high-dimensional, they may reside in a lower dimensional manifold. We investigated the manifold structure of 2D CTC ROIs by use of the Laplacian eigenmaps technique. We compared a support vector machine (SVM) classifier with the Laplacian eigenmaps-based dimensionality-reduced ROIs with massive-training support vector regression (MTSVR) in reduction of false positive (FP) detections. The Laplacian eigenmaps-based SVM classifier removed 16.0% (78/489) of FPs without any loss of polyps in a leave-one-lesion-out cross-validation test, whereas the MTSVR removed 49.9% (244/489); thus, yielded a 96.6% by-polyp sensitivity at an FP rate of 2.4 (254/106) per patient.