DeepMV: Fully Automatic Ultrasound Segmentation for Patient Specific Mitral Valve Modelling

Recently, developments have been made towards modelling patient-specific deformable mitral valves from transesophageal echocar-diography (TEE). Thus far, a major limitation in the workflow has been the manual process of segmentation and model profile definition. Completing a manual segmentation from 3D TEE can take upwards of two hours, and existing automated segmentation approaches have limitations in both computation time and accuracy. Streamlining the process of segmenting the valve and generating a surface mold is important for the scalability and accuracy of patient-specific mitral valve modelling. We present DeepMV, a fully automatic, deep learning based mitral valve segmentation approach that can quickly and accurately extract the geometry of the mitral valve directly from TEE volumes. We developed and tested our model on a data set comprising 48 diagnostic TEE volumes with corresponding segmentations from mitral valve intervention patients. Our proposed pipeline is based on the Residual UNet architecture with five layers. Evaluation of our proposed pipeline was assessed using manual segmentations performed by two clinicians as a gold-standard. The comparisons are made using the mean absolute surface distance (MASD) between the boundaries of the complete segmentations, as well as the 95 % Hausdorff distances. DeepMV achieves a MASD of 0.59±0.23 mm and average 95% Hausdorff distance of 1.99±1.14 mm. Additionally, we report a Dice score of 0.81. The resulting segmentations from our approach successfully replicate gold-standard segmentations with improved performance over existing state-of-the-art methods. DeepMV improves the workflow of the mitral valve modelling process by reducing the time required for completing an accurate mitral valve segmentation, and providing more consistent results by removing user variability from the segmentation process.

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