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.

翻译：为辅助心脏超声诊断，临床实践已确立若干标准心脏切面视图，这些视图为诊断测量提供参考基准，并定义了图像采集的视口。自动视图识别旨在将采集图像归类至标准视图类别。虽然深度学习技术在此任务中取得显著成效，但受心脏结构定位、姿态、潜在遮挡等因素影响，现有方法仍难以充分验证图像是否适用于特定测量。本方法超越传统视图分类框架，通过构建心脏三维网格重建实现分割、姿态估计等多项下游任务。本研究探索采用图卷积技术学习三维心脏网格（类似自然图像中用于人体姿态估计的三维网格建模方法）。鉴于完整标注的三维图像资源有限，我们通过训练对抗性去噪扩散模型，从三维网格生成合成超声图像。实验在合成图像与临床病例中展开视图识别与结构检测验证，该方法在合成图像上表现优异，且尽管仅基于合成数据训练，在临床图像应用中已展现潜力。通过此概念验证，我们旨在揭示图神经网络改进心脏视图识别的优势，最终提升心脏诊断效率。