Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure

Jinbo Bi, Jianming Liang

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

22 Scopus citations

Abstract

We propose a novel classification approach for automatically detecting pulmonary embolism (PE) from computedtomography-angiography images. Unlike most existing approaches that require vessel segmentation to restrict the search space for PEs, our toboggan-based candidate generator is capable of searching the entire lung for any suspicious regions quickly and efficiently. We then exploit the spatial information supplied in the vascular structure as a post-candidate-generation step by designing classifiers with geodesic distances between candidates along the vascular tree. Moreover, a PE represents a cluster of voxels in an image, and thus multiple candidates can be associated with a single PE and the PE is identified if any of its candidates is correctly classified. The proposed algorithm also provides an efficient solution to the problem of learning with multiple positive instances. Our clinical studies with 177 clinical cases demonstrate that the proposed approach outperforms existing detection methods, achieving 81% sensitivity on an independent test set at 4 false positives per study.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 12 2007
Externally publishedYes
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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