A comprehensive guidance view for cardiac interventional surgery can be
provided by the real-time fusion of the intraoperative 2D images and
preoperative 3D volume based on the ultrasound frame-to-volume registration.
However, cardiac ultrasound images are characterized by a low signal-to-noise
ratio and small differences between adjacent frames, coupled with significant
dimension variations between 2D frames and 3D volumes to be registered,
resulting in real-time and accurate cardiac ultrasound frame-to-volume
registration being a very challenging task. This paper introduces a lightweight
end-to-end Cardiac Ultrasound frame-to-volume Registration network, termed
CU-Reg. Specifically, the proposed model leverages epicardium prompt-guided
anatomical clues to reinforce the interaction of 2D sparse and 3D dense
features, followed by a voxel-wise local-global aggregation of enhanced
features, thereby boosting the cross-dimensional matching effectiveness of
low-quality ultrasound modalities. We further embed an inter-frame
discriminative regularization term within the hybrid supervised learning to
increase the distinction between adjacent slices in the same ultrasound volume
to ensure registration stability. Experimental results on the reprocessed CAMUS
dataset demonstrate that our CU-Reg surpasses existing methods in terms of
registration accuracy and efficiency, meeting the guidance requirements of
clinical cardiac interventional surgery.

通过超声帧到体积的实时融合，基于术中 2D 图像和术前 3D 体积，提供了一个全面的心脏介入手术指导视图。然而，由于心脏超声图像在信噪比低、相邻帧之间差异小，以及 2D 帧和 3D 体积之间尺寸变化显著，因此实现实时准确的心脏超声帧到体积的配准是一项非常具有挑战性的任务。本文提出了一种轻量级的端到端心脏超声帧到体积配准网络，称为 CU-Reg。具体而言，该模型利用心外膜引导的解剖线索来加强 2D 稀疏和 3D 密集特征的交互作用，然后通过体素级的局部全局特征汇聚增强特征，从而提高低质量超声模态的跨维度匹配效果。我们进一步在混合监督学习中嵌入了帧间判别性正则化项，以增加同一超声体积中相邻切片之间的区别，确保配准的稳定性。在重新处理的 CAMUS 数据集上的实验结果表明，我们的 CU-Reg 在配准精度和效率方面超过了现有方法，满足了临床心脏介入手术的指导要求。

心外膜及时引导的实时心脏超声帧到体积注册

Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume  Registration

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.

使用图卷积技术学习三维心脏网格，通过生成合成超声图像并进行实验验证，展示了图对于改善心脏视图识别的潜力，并最终提高心脏诊断效率。

基于图卷积神经网络的自动超声心动图视角识别：一种整体方法

Graph Convolutional Neural Networks for Automated Echocardiography View  Recognition: A Holistic Approach

We propose a novel pipeline for the generation of synthetic images via
Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac ultrasound
semantic label maps. We show that these synthetic images can serve as a viable
substitute for real data in the training of deep-learning models for medical
image analysis tasks such as image segmentation. To demonstrate the
effectiveness of this approach, we generated synthetic 2D echocardiography
images and trained a neural network for segmentation of the left ventricle and
left atrium. The performance of the network trained on exclusively synthetic
images was evaluated on an unseen dataset of real images and yielded mean Dice
scores of 88.5 $\pm 6.0$ , 92.3 $\pm 3.9$, 86.3 $\pm 10.7$ \% for left
ventricular endocardial, epicardial and left atrial segmentation respectively.
This represents an increase of $9.09$, $3.7$ and $15.0$ \% in Dice scores
compared to the previous state-of-the-art. The proposed pipeline has the
potential for application to a wide range of other tasks across various medical
imaging modalities.

本文提出了一种通过心脏超声语义标签图引导的去噪扩散概率模型（DDPMs）生成合成图像的新型流程，展示了这些合成图像可以作为医学影像分析任务的深度学习模型训练中真实数据的可行替代品，包括图像分割，并在未见实际数据的数据集上进行了评估，与现有技术相比，Dice 分数分别增加了 9.09％，3.7％和 15.0％，该流程具有应用于各种医学成像模态的各种其他任务的潜力。