A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography

• URL: http://arxiv.org/abs/2302.10634v1
• Date: Tue, 21 Feb 2023 12:48:44 GMT
• Title: A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography
• Authors: Riccardo Munaf\`o, Simone Saitta, Giacomo Ingallina, Paolo Denti, Francesco Maisano, Eustachio Agricola, Alberto Redaelli, Emiliano Votta
• Abstract summary: 3D transesophageal echocardiography (3DTEE) is the recommended method for diagnosing mitral regurgitation (MR) manual TEE segmentations are time-consuming and prone to intra-operator variability, affecting the reliability of the measurements. We developed a fully automated pipeline using a 3D convolutional neural network (CNN) to segment MV substructures and quantify MV anatomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D transesophageal echocardiography (3DTEE), is the recommended method for diagnosing mitral regurgitation (MR). 3DTEE provides a high-quality 3D image of the mitral valve (MV), allowing for precise segmentation and measurement of the regurgitant valve anatomy. However, manual TEE segmentations are time-consuming and prone to intra-operator variability, affecting the reliability of the measurements. To address this, we developed a fully automated pipeline using a 3D convolutional neural network (CNN) to segment MV substructures (annulus, anterior leaflet, and posterior leaflet) and quantify MV anatomy. The 3D CNN, based on a multi-decoder residual U-Net architecture, was trained and tested on a dataset comprising 100 3DTEE images with corresponding segmentations. Within the pipeline, a custom algorithm refines the CNN-based segmentations and extracts MV models, from which anatomical landmarks and features are quantified. The accuracy of the proposed method was assessed using Dice score and mean surface distance (MSD) against ground truth segmentations, and the extracted anatomical parameters were compared against a semiautomated commercial software TomTec Image Arena. The trained 3D CNN achieved an average Dice score of 0.79 and MSD of 0.47 mm for the combined segmentation of the annulus, anterior and posterior leaflet. The proposed CNN architecture outperformed a baseline residual U-Net architecture in MV substructure segmentation, and the refinement of the predicted annulus segmentation improved MSD by 8.36%. The annular and leaflet linear measurements differed by less than 7.94 mm and 3.67 mm, respectively, compared to the 3D measurements obtained with TomTec Image Arena. The proposed pipeline was faster than the commercial software, with a modeling time of 12.54 s and a quantification time of 54.42 s.

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