Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for
diagnostic and interventional procedures. However, using it effectively
requires extensive training due to the intricate nature of image acquisition
and interpretation. To enhance the efficiency of novice sonographers and reduce
variability in scan acquisitions, we propose a novel ultrasound (US) navigation
assistance method based on contrastive learning as goal-conditioned
reinforcement learning (GCRL). We augment the previous framework using a novel
contrastive patient batching method (CPB) and a data-augmented contrastive
loss, both of which we demonstrate are essential to ensure generalization to
anatomical variations across patients. The proposed framework enables
navigation to both standard diagnostic as well as intricate interventional
views with a single model. Our method was developed with a large dataset of 789
patients and obtained an average error of 6.56 mm in position and 9.36 degrees
in angle on a testing dataset of 140 patients, which is competitive or superior
to models trained on individual views. Furthermore, we quantitatively validate
our method's ability to navigate to interventional views such as the Left
Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in
providing valuable guidance during transesophageal ultrasound examinations,
contributing to the advancement of skill acquisition for cardiac ultrasound
practitioners.

通过对比学习为目标条件强化学习的超声导航辅助方法能够有效提高新手超声医生的效率，并降低扫描中获取的变异性。

超声导航引导的目标条件强化学习

Goal-conditioned reinforcement learning for ultrasound navigation  guidance

Statistical Shape Modeling (SSM) is an effective method for quantitatively
analyzing anatomical variations within populations. However, its utility is
limited by the need for manual segmentations of anatomies, a task that relies
on the scarce expertise of medical professionals. Recent advances in deep
learning have provided a promising approach that automatically generates
statistical representations from unsegmented images. Once trained, these deep
learning-based models eliminate the need for manual segmentation for new
subjects. Nonetheless, most current methods still require manual pre-alignment
of image volumes and specifying a bounding box around the target anatomy prior
for inference, resulting in a partially manual inference process. Recent
approaches facilitate anatomy localization but only estimate statistical
representations at the population level. However, they cannot delineate anatomy
directly in images and are limited to modeling a single anatomy. Here, we
introduce MASSM, a novel end-to-end deep learning framework that simultaneously
localizes multiple anatomies in an image, estimates population-level
statistical representations, and delineates each anatomy. Our findings
emphasize the crucial role of local correspondences, showcasing their
indispensability in providing superior shape information for medical imaging
tasks.

MASSM 是一种新的端到端深度学习框架，能够同时在图像中定位多个解剖结构，估计群体级别的统计表示，并勾画出每个解剖结构。

基于图像的多解剖统计形状建模的端到端深度学习框架 MASSM

MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy  Statistical Shape Modeling Directly From Images

The Circle of Willis (CoW) is the part of cerebral vasculature responsible
for delivering blood to the brain. Understanding the diverse anatomical
variations and configurations of the CoW is paramount to advance research on
cerebrovascular diseases and refine clinical interventions. However,
comprehensive investigation of less prevalent CoW variations remains
challenging because of the dominance of a few commonly occurring
configurations. We propose a novel generative approach utilising a conditional
latent diffusion model with shape and anatomical guidance to generate realistic
3D CoW segmentations, including different phenotypical variations. Our
conditional latent diffusion model incorporates shape guidance to better
preserve vessel continuity and demonstrates superior performance when compared
to alternative generative models, including conditional variants of 3D GAN and
3D VAE. We observed that our model generated CoW variants that are more
realistic and demonstrate higher visual fidelity than competing approaches with
an FID score 53\% better than the best-performing GAN-based model.

利用条件潜在扩散模型与形状和解剖导向相结合，提出了一种新颖的生成方法，能够生成逼真的三维 Willis 环分割，包括不同的表型变异，性能优于其他生成模型，包括条件 3D GAN 和 3D VAE。