Coronary angiography is the gold standard imaging technique for studying and
diagnosing coronary artery disease. However, the resulting 2D X-ray projections
lose 3D information and exhibit visual ambiguities. In this work, we aim to
establish dense correspondence in multi-view angiography, serving as a
fundamental basis for various clinical applications and downstream tasks. To
overcome the challenge of unavailable annotated data, we designed a data
simulation pipeline using 3D Coronary Computed Tomography Angiography (CCTA).
We formulated the problem of dense correspondence estimation as a query
matching task over all points of interest in the given views. We established
point-to-point query matching and advanced it to curve-to-curve correspondence,
significantly reducing errors by minimizing ambiguity and improving topological
awareness. The method was evaluated on a set of 1260 image pairs from different
views across 8 clinically relevant angulation groups, demonstrating compelling
results and indicating the feasibility of establishing dense correspondence in
multi-view angiography.

通过设计数据模拟流程，将密集对应问题转化为所有感兴趣点的查询匹配任务，并提高了拓扑意识，从而建立了多视角病灶密集对应的可行性。

建立多视角冠状动脉造影中的密集对应：从点对点到曲线对曲线的查询匹配

Towards Establishing Dense Correspondence on Multiview Coronary  Angiography: From Point-to-Point to Curve-to-Curve Query Matching

Coronary artery stenosis is a critical health risk, and its precise
identification in Coronary Angiography (CAG) can significantly aid medical
practitioners in accurately evaluating the severity of a patient's condition.
The complexity of coronary artery structures combined with the inherent noise
in X-ray images poses a considerable challenge to this task. To tackle these
obstacles, we introduce a semi-supervised approach for cardiovascular stenosis
segmentation. Our strategy begins with data augmentation, specifically tailored
to replicate the structural characteristics of coronary arteries. We then apply
a pseudo-label-based semi-supervised learning technique that leverages the data
generated through our augmentation process. Impressively, our approach
demonstrated an exceptional performance in the Automatic Region-based Coronary
Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) Stenosis
Detection Algorithm challenge by utilizing a single model instead of relying on
an ensemble of multiple models. This success emphasizes our method's capability
and efficiency in providing an automated solution for accurately assessing
stenosis severity from medical imaging data.

我们介绍了一种用于心血管狭窄分割的半监督方法，通过数据增广和利用伪标签的学习技术，实现了在冠状动脉造影图像中准确评估病患情况的自动诊断算法。

SSASS：半监督式狭窄分割方法

SSASS: Semi-Supervised Approach for Stenosis Segmentation

Coronary angiography remains the gold standard for diagnosis of coronary
artery disease, the most common cause of death worldwide. While this procedure
is performed more than 2 million times annually, there remain few methods for
fast and accurate automated measurement of disease and localization of coronary
anatomy. Here, we present our solution to the Automatic Region-based Coronary
Artery Disease diagnostics using X-ray angiography images (ARCADE) challenge
held at MICCAI 2023. For the artery segmentation task, our three-stage approach
combines preprocessing and feature selection by classical computer vision to
enhance vessel contrast, followed by an ensemble model based on YOLOv8 to
propose possible vessel candidates by generating a vessel map. A final
segmentation is based on a logic-based approach to reconstruct the coronary
tree in a graph-based sorting method. Our entry to the ARCADE challenge placed
3rd overall. Using the official metric for evaluation, we achieved an F1 score
of 0.422 and 0.4289 on the validation and hold-out sets respectively.

我们提出了一种基于 X 射线血管造影图像的、能够自动化诊断冠状动脉疾病的自动区域冠状动脉疾病诊断的方法，该方法结合了经典计算机视觉的预处理和特征选择，通过增强血管对比度来提高血管分割的准确性，并利用 YOLOv8 生成血管图像并基于逻辑推理的方法重构冠状血管树以实现最终分割，我们在 ARCADE 挑战中获得了第三名，验证集和保留集的 F1 得分分别为 0.422 和 0.4289。

YOLO-Angio：冠状动脉解剖分割算法

YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation

Coronary angiography continues to serve as the primary method for diagnosing
coronary artery disease (CAD), which is the leading global cause of mortality.
The severity of CAD is quantified by the location, degree of narrowing
(stenosis), and number of arteries involved. In current practice, this
quantification is performed manually using visual inspection and thus suffers
from poor inter- and intra-rater reliability. The MICCAI grand challenge:
Automatic Region-based Coronary Artery Disease diagnostics using the X-ray
angiography imagEs (ARCADE) curated a dataset with stenosis annotations, with
the goal of creating an automated stenosis detection algorithm. Using a
combination of machine learning and other computer vision techniques, we
propose the architecture and algorithm StenUNet to accurately detect stenosis
from X-ray Coronary Angiography. Our submission to the ARCADE challenge placed
3rd among all teams. We achieved an F1 score of 0.5348 on the test set, 0.0005
lower than the 2nd place.

提出了一种用于从 X 射线冠状动脉造影图中准确检测狭窄的架构和算法 StenUNet，通过结合机器学习和其他计算机视觉技术，在 ARCADE 挑战中获得第三名，测试数据集上达到了 0.5348 的 F1 得分，比第二名低了 0.0005。