Fast and Accurate Tracking of Highly Deformable Heart Valves with Locally Constrained Level Sets

Abstract

This paper focuses on the automatic quantitative performance analysis of bioprosthetic heart valves from video footage acquired during in vitro testing. Bioprosthetic heart valves, mimicking the shape and functionality of a human heart valve, are routinely used in valve replacement procedures to substitute defective native valves. Their reliability in both functionality and durability is crucial to the patients' well-being, as such, valve designs must be rigorously tested before deployment. A key quality metric of a heart valve design is the cyclical temporal evolution of the valve's area. This metric is typically computed manually from input video data, a time-consuming and error-prone task. We propose a novel, cost-effective approach for the automatic tracking and segmentation of valve orifices that integrates a probabilistic motion boundary model into a distance regularized level set evolution formulation. The proposed method constrains the level set evolution domain using data about characteristic motion patterns of heart valves. Experiments including comparisons with two other methods demonstrate the value of the proposed approach on three levels: an improved segmented orifice shape accuracy, a greater computational efficiency, and a better ability to identify video frames with orifice area content (open valve).