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Dept. of Computer Sc. » Pattern Recognition » Our Team » Voigt, Ingmar » Projects » Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease
Dipl.-Ing. Ingmar VoigtAlumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-NürnbergPersonalized healthcare with robust patient-specific models of anatomy and function
Project Description
Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart diseaseDisorders 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 graphs based 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.
Publications
Voigt, Ingmar; Vitanovski, Dime; Ionasec, Razvan Ioan; Tsymbal, Alexey; Georgescu, Bogdan; Zhou, Shaohua Kevin; Huber, Martin; Navab, Nassir; Hornegger, Joachim; Comaniciu, Dorin
Media
Acknowledgements
Part of this work has been done in the framework of the EU project Health-e-Child (IST 2004-027749). |