Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease

Ingmar Voigt, Dime Vitanovski, Razvan I. Ionasec, Alexey Tsymal, Bogdan Georgescu, Shaohua K. Zhou, Martin Huber, Nassir Navab, Joachim Hornegger, Dorin Comaniciu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2010
Subtitle of host publicationImage Processing
EditionPART 1
DOIs
StatePublished - 2010
EventMedical Imaging 2010: Image Processing - San Diego, CA, United States
Duration: 14 Feb 201016 Feb 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
NumberPART 1
Volume7623
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2010: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA
Period14/02/1016/02/10

Keywords

  • case retrieval
  • decision support
  • discriminative distance function
  • machine learning
  • physiological valve modeling

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