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

Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial
diseases, mainly due to the advantages of being non-invasive, radiation-free,
and real-time. In order to provide additional information to assist clinicians
in diagnosis, the tubular structures are often segmented from US images. To
improve the artery segmentation accuracy and stability during scans, this work
presents a novel pulsation-assisted segmentation neural network (PAS-NN) by
explicitly taking advantage of the cardiac-induced motions. Motion
magnification techniques are employed to amplify the subtle motion within the
frequency band of interest to extract the pulsation signals from sequential US
images. The extracted real-time pulsation information can help to locate the
arteries on cross-section US images; therefore, we explicitly integrated the
pulsation into the proposed PAS-NN as attention guidance. Notably, a robotic
arm is necessary to provide stable movement during US imaging since magnifying
the target motions from the US images captured along a scan path is not
manually feasible due to the hand tremor. To validate the proposed robotic US
system for imaging arteries, experiments are carried out on volunteers' carotid
and radial arteries. The results demonstrated that the PAS-NN could achieve
comparable results as state-of-the-art on carotid and can effectively improve
the segmentation performance for small vessels (radial artery).

提出了一种新颖的脉动辅助分割神经网络 (PAS-NN)，通过明确利用心脏诱导的动作，运用运动放大技术来放大兴趣频带内的微小运动从而提取连续超声图像中的脉动信号，提高血管分割的准确性和稳定性，并通过创新性地整合脉动作为注意力指导，有效改善超声图像中小血管的分割性能。