Physically-coherent Extraction of Mitral Valve Chordae

International audienceSurgical repair of the mitral valve is challenging as it is difficult to anticipate the outcome of any modification of the valve structure, particularly the tendinous chordae. Recent works on computer-based models of mitral valve behavior rely on manual extraction of the complex valve geometry, which is tedious and requires a high level of expertise. On the contrary, we propose a method to segment the chordae with little human supervision. The effectiveness of our approach is shown by comparing our segmen-tation to the manual delineation via the simulation of the closed valve state

Toward an automatic segmentation of mitral valve chordae

International audienceHeart disease is the leading cause of death in the developed world. Cardiac pathologies include abnormal closure of the mitral valve, which can be treated by surgical operations, but the repair outcome varies greatly based on the experience of the surgeon. Simulating the procedure with a computer-based tool can greatly improve valve repair. Various teams are working on biomechanical models to compute the valve behaviour during peak systole. Although they use an accurate finite element method, they also use a tedious manual segmentation of the valve. Providing means to automatically segment the chordae and the leaflets would allow significant progress in the perspective of simulating the surgical gesture for the mitral valve repair. Valve chordae are generalized cylinders: Instead of being limited to a line, the central axis is a continuous curve. Instead of a constant radius, the radius varies along the axis. In most of the cases chordae sections are flattened ellipses and classical model-based methods commonly used for vessel enhancement or vessel segmentation fail. In this paper, we exploit the fact that there are no other generalized cylinders than the chordae in the micro CT scan and we propose a topology-based method for the chordae extraction. This approach is flexible and only requires the knowledge of an upper bound of the maximum chordae radius. Examples of segmentation are provided on three porcine datasets. The reliability of the segmentation is proved with a dataset where the ground truth is available