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.
Disorders of the mitral valve are second most frequent, cumulating 14 percent of total number of deaths caused
by Valvular Heart Disease each year in the United States and require elaborate clinical management. Visual
and quantitative evaluation of the valve is an important step in the clinical workflow according to experts
as knowledge about mitral morphology and dynamics is crucial for interventional planning. Traditionally
this involves examination and metric analysis of 2D images comprising potential errors being intrinsic to the
method. Recent commercial solutions are limited to specific anatomic components, pathologies and a single
phase of cardiac 4D acquisitions only. This paper introduces a novel approach for morphological and functional
quantification of the mitral valve based on a 4D model estimated from ultrasound data. A physiological model of
the mitral valve, covering the complete anatomy and eventual shape variations, is generated utilizing parametric
spline surfaces constrained by topological and geometrical prior knowledge. The 4D model's parameters are
estimated for each patient using the latest discriminative learning and incremental searching techniques. Precise
evaluation of the anatomy using model-based dynamic measurements and advanced visualization are enabled
through the proposed approach in a reliable, repeatable and reproducible manner. The efficiency and accuracy
of the method is demonstrated through experiments and an initial validation based on clinical research results.
To the best of our knowledge this is the first time such a patient specific 4D mitral valve model is proposed,
covering all of the relevant anatomies and enabling to model the common pathologies at once.