Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
This work addresses the challenge of verifying image suitability for cardiac measurements in echocardiography, which could improve efficiency in cardiac diagnosis, though it appears incremental as it builds on existing techniques for 3D mesh learning.
The paper tackled the problem of automated echocardiography view recognition by incorporating 3D mesh reconstruction of the heart using graph convolutional neural networks, achieving good performance on synthetic images and showing potential on clinical images despite being trained only on synthetic data.
To facilitate diagnosis on cardiac ultrasound (US), clinical practice has established several standard views of the heart, which serve as reference points for diagnostic measurements and define viewports from which images are acquired. Automatic view recognition involves grouping those images into classes of standard views. Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures. Our approach goes beyond view classification and incorporates a 3D mesh reconstruction of the heart that enables several more downstream tasks, like segmentation and pose estimation. In this work, we explore learning 3D heart meshes via graph convolutions, using similar techniques to learn 3D meshes in natural images, such as human pose estimation. As the availability of fully annotated 3D images is limited, we generate synthetic US images from 3D meshes by training an adversarial denoising diffusion model. Experiments were conducted on synthetic and clinical cases for view recognition and structure detection. The approach yielded good performance on synthetic images and, despite being exclusively trained on synthetic data, it already showed potential when applied to clinical images. With this proof-of-concept, we aim to demonstrate the benefits of graphs to improve cardiac view recognition that can ultimately lead to better efficiency in cardiac diagnosis.