Coronary artery disease (CAD) is one of the most prevalent diseases in the
cardiovascular field and one of the major contributors to death worldwide.
Computed Tomography Angiography (CTA) images are regarded as the authoritative
standard for the diagnosis of coronary artery disease, and by performing vessel
segmentation and stenosis detection on CTA images, physicians are able to
diagnose coronary artery disease more accurately. In order to combine the
advantages of both the base model and the domain-specific model, and to achieve
high-precision and fully-automatic segmentation and detection with a limited
number of training samples, we propose a novel architecture, SAM-VMNet, which
combines the powerful feature extraction capability of MedSAM with the
advantage of the linear complexity of the visual state-space model of VM-UNet,
giving it faster inferences than Vision Transformer with faster inference speed
and stronger data processing capability, achieving higher segmentation accuracy
and stability for CTA images. Experimental results show that the SAM-VMNet
architecture performs excellently in the CTA image segmentation task, with a
segmentation accuracy of up to 98.32% and a sensitivity of up to 99.33%, which
is significantly better than other existing models and has stronger domain
adaptability. Comprehensive evaluation of the CTA image segmentation task shows
that SAM-VMNet accurately extracts the vascular trunks and capillaries,
demonstrating its great potential and wide range of application scenarios for
the vascular segmentation task, and also laying a solid foundation for further
stenosis detection.

我们提出了一种新颖的体系结构 SAM-VMNet，它结合了 MedSAM 的强大特征提取能力和 VM-UNet 的线性复杂性视觉状态空间模型的优势，具有更快的推理速度和更强的数据处理能力，实现了 CTA 图像的更高分割准确性和稳定性。

SAM-VMNet：冠状动脉造影血管分割的深度神经网络

SAM-VMNet: Deep Neural Networks For Coronary Angiography Vessel  Segmentation

Coronary artery disease (CAD) is one of the most common causes of death in
the European Union and the USA. The crucial biomarker in its diagnosis is
called Fractional Flow Reserve (FFR) and its in-vivo measurement is obtained
via an invasive diagnostic technique in the form of coronagraphy. In order to
address the invasive drawbacks associated with a procedure, a new approach
virtual FFR (vFFR) measurement has emerged in recent years. This technique
involves using computed tomography angiography (CTA) to obtain virtual
measurements of FFR. By utilizing Computational Fluid Dynamics (CFD), vFFR
estimates can be derived from CTA data, providing a promising in-silico
alternative to traditional methods. However, the widespread adoption of vFFR
from CTA as a diagnostic technique is hindered by two main challenges: time and
computational requirements. In this work, we explore the usage of deep learning
techniques as surrogate CFD engine models in the task of vFFR estimation in
coronary arteries to drastically limit the required time and computational
costs without a major drop in quality. We propose a novel approach to vFFR
estimation by representing the input vessel geometry as a point cloud and
utilizing the hybrid neural network that learns geometry representation based
on both explicitly and implicitly given features. We evaluate the method from
the clinical point of view and showcase that it can serve as a compelling
replacement for commonly utilized CFD-based approaches.

冠状动脉疾病是欧盟和美国最常见的死因之一，其中关键的生物标志物是分数流量减少 (FFR)，通过侵入性诊断技术冠状动脉造影获得其体内测量。为了解决侵入性的缺点，近年来出现了一种名为虚拟 FFR (vFFR) 测量的新方法。本文中，我们探讨了使用深度学习技术作为替代 CFD 引擎模型在冠状动脉 vFFR 估计中的任务中，以极大地减少所需时间和计算成本而不会显著降低质量的方法。我们提出了一种新颖的 vFFR 估计方法，通过将输入血管几何表示为点云，并利用学习几何表示的混合神经网络，基于明确和隐式给定的特征。我们从临床角度评估了该方法，并展示它可以作为常用的基于 CFD 的方法的有力替代。

使用深度学习估计冠状动脉中的 FFR

Estimation of FFR in coronary arteries with deep learning

Ischemic strokes are often caused by large vessel occlusions (LVOs), which
can be visualized and diagnosed with Computed Tomography Angiography scans. As
time is brain, a fast, accurate and automated diagnosis of these scans is
desirable. Human readers compare the left and right hemispheres in their
assessment of strokes. A large training data set is required for a standard
deep learning-based model to learn this strategy from data. As labeled medical
data in this field is rare, other approaches need to be developed. To both
include the prior knowledge of side comparison and increase the amount of
training data, we propose an augmentation method that generates artificial
training samples by recombining vessel tree segmentations of the hemispheres or
hemisphere subregions from different patients. The subregions cover vessels
commonly affected by LVOs, namely the internal carotid artery (ICA) and middle
cerebral artery (MCA). In line with the augmentation scheme, we use a
3D-DenseNet fed with task-specific input, fostering a side-by-side comparison
between the hemispheres. Furthermore, we propose an extension of that
architecture to process the individual hemisphere subregions. All
configurations predict the presence of an LVO, its side, and the affected
subregion. We show the effect of recombination as an augmentation strategy in a
5-fold cross validated ablation study. We enhanced the AUC for patient-wise
classification regarding the presence of an LVO of all investigated
architectures. For one variant, the proposed method improved the AUC from 0.73
without augmentation to 0.89. The best configuration detects LVOs with an AUC
of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while
accurately predicting the affected side.

通过使用图像增强技术和深度学习算法，针对缺乏标记的医学数据，提出了一种诊断大血管闭塞性卒中的方法，将左右半球影像进行侧面比较来预测卒中的位置。