Precise localization of coronary arteries in Computed Tomography (CT) scans
is critical from the perspective of medical assessment of coronary artery
disease. Although various methods exist that offer high-quality segmentation of
coronary arteries in cardiac contrast-enhanced CT scans, the potential of less
invasive, non-contrast CT in this area is still not fully exploited. Since such
fine anatomical structures are hardly visible in this type of medical images,
the existing methods are characterized by high recall and low precision, and
are used mainly for filtering of atherosclerotic plaques in the context of
calcium scoring. In this paper, we address this research gap and introduce a
deep learning algorithm for segmenting coronary arteries in multi-vendor
ECG-gated non-contrast cardiac CT images which benefits from a novel framework
for semi-automatic generation of Ground Truth (GT) via image registration. We
hypothesize that the proposed GT generation process is much more efficient in
this case than manual segmentation, since it allows for a fast generation of
large volumes of diverse data, which leads to well-generalizing models. To
investigate and thoroughly evaluate the segmentation quality based on such an
approach, we propose a novel method for manual mesh-to-image registration,
which is used to create our test-GT. The experimental study shows that the
trained model has significantly higher accuracy than the GT used for training,
and leads to the Dice and clDice metrics close to the interrater variability.

本文提出了一种基于深度学习算法的多供应商心电图门控无对比剂心脏 CT 图像冠状动脉分割的方法，该方法通过图像配准实现了半自动生成 Ground Truth，研究结果表明训练模型具有比训练时使用的 Ground Truth 更高的准确性，并且 Dice 和 clDice 指标接近评定者之间的差异。

无造影钙化计分 CT 图像中的冠状动脉分割利用深度学习

Coronary artery segmentation in non-contrast calcium scoring CT images  using deep learning

Accurate segmentation of coronary arteries is a pivotal process in assessing
cardiovascular diseases. However, the intricate structure of the cardiovascular
system presents significant challenges for automatic segmentation, especially
when utilizing methodologies like the SYNTAX Score, which relies extensively on
detailed structural information for precise risk stratification. To address
these difficulties and cater to this need, we present MPSeg, an innovative
multi-phase strategy designed for coronary artery segmentation. Our approach
specifically accommodates these structural complexities and adheres to the
principles of the SYNTAX Score. Initially, our method segregates vessels into
two categories based on their unique morphological characteristics: Left
Coronary Artery (LCA) and Right Coronary Artery (RCA). Specialized ensemble
models are then deployed for each category to execute the challenging
segmentation task. Due to LCA's higher complexity over RCA, a refinement model
is utilized to scrutinize and correct initial class predictions on segmented
areas. Notably, our approach demonstrated exceptional effectiveness when
evaluated in the Automatic Region-based Coronary Artery Disease diagnostics
using x-ray angiography imagEs (ARCADE) Segmentation Detection Algorithm
challenge at MICCAI 2023.

我们介绍了一种名为 MPSeg 的创新多阶段策略，专门用于冠状动脉分割，其采用了分离左冠状动脉和右冠状动脉以及集成模型等方法，针对冠状动脉的结构复杂性和 SYNTAX Score 的原则进行了设计和优化，并在 MICCAI 2023 年的 ARCADE 挑战中表现出了卓越的效果。

MPSeg：冠状动脉分割的多阶段策略

MPSeg : Multi-Phase strategy for coronary artery Segmentation

Automatic labeling of coronary arteries is an essential task in the practical
diagnosis process of cardiovascular diseases. For experienced radiologists, the
anatomically predetermined connections are important for labeling the artery
segments accurately, while this prior knowledge is barely explored in previous
studies. In this paper, we present a new framework called TopoLab which
incorporates the anatomical connections into the network design explicitly.
Specifically, the strategies of intra-segment feature aggregation and
inter-segment feature interaction are introduced for hierarchical segment
feature extraction. Moreover, we propose the anatomy-aware connection
classifier to enable classification for each connected segment pair, which
effectively exploits the prior topology among the arteries with different
categories. To validate the effectiveness of our method, we contribute
high-quality annotations of artery labeling to the public orCaScore dataset.
The experimental results on both the orCaScore dataset and an in-house dataset
show that our TopoLab has achieved state-of-the-art performance.

对冠状动脉的自动标记是心血管疾病实际诊断过程中的关键任务。本研究提出了一种名为 TopoLab 的新框架，明确地将解剖连接性纳入网络设计中，并引入了区段内特征聚合和区段间特征交互的策略，实现了分级分段特征提取；此外，我们还提出了一种解剖感知连接分类器，能够对每个连接的区段对进行分类，从而有效地利用不同类别动脉之间的先验拓扑关系。通过对公开的 orCaScore 数据集和内部数据集进行实验证明了我们方法的有效性，并取得了最新的研究成果。