Coronary artery diseases are among the leading causes of mortality worldwide.
Timely and accurate diagnosis, facilitated by precise coronary artery
segmentation, is pivotal in changing patient outcomes. In the realm of
biomedical imaging, convolutional neural networks, especially the U-Net
architecture, have revolutionised segmentation processes. However, one of the
primary challenges remains the lack of benchmarking datasets specific to
coronary arteries. However through the use of the recently published public
dataset ASOCA, the potential of deep learning for accurate coronary
segmentation can be improved. This paper delves deep into examining the
performance of 25 distinct encoder-decoder combinations. Through analysis of
the 40 cases provided to ASOCA participants, it is revealed that the
EfficientNet-LinkNet combination, serving as encoder and decoder, stands out.
It achieves a Dice coefficient of 0.882 and a 95th percentile Hausdorff
distance of 4.753. These findings not only underscore the superiority of our
model in comparison to those presented at the MICCAI 2020 challenge but also
set the stage for future advancements in coronary artery segmentation, opening
doors to enhanced diagnostic and treatment strategies.

经过对 25 种不同的编码器 - 解码器组合的分析，发现 EfficientNet-LinkNet 组合在冠状动脉分割中表现出色，其 Dice 系数为 0.882，95％分位数的 Hausdorff 距离为 4.753，这一发现不仅凸显了我们模型相较于 MICCAI 2020 挑战中的其他模型的优越性，也为冠状动脉分割的未来进展奠定了基础，为改进诊断和治疗策略开辟了新的可能性。

评估编码器 - 解码器架构对冠状动脉分割的鲁棒性

Assessing Encoder-Decoder Architectures for Robust Coronary Artery  Segmentation

Coronary Artery Diseases(CADs) though preventable are one of the leading
causes of death and disability. Diagnosis of these diseases is often difficult
and resource intensive. Segmentation of arteries in angiographic images has
evolved as a tool for assistance, helping clinicians in making accurate
diagnosis. However, due to the limited amount of data and the difficulty in
curating a dataset, the task of segmentation has proven challenging. In this
study, we introduce the idea of using pseudolabels as a data augmentation
technique to improve the performance of the baseline Yolo model. This method
increases the F1 score of the baseline by 9% in the validation dataset and by
3% in the test dataset.

冠状动脉疾病（CAD）是导致死亡和残疾的主要原因之一，其诊断常具有困难和资源密集性。本研究提出了使用伪标签作为数据增强技术来改进基线 Yolo 模型性能的想法，在验证数据集中将基线的 F1 分数提高了 9％，在测试数据集中提高了 3％。

自动基于区域的冠状动脉分割中的伪标签数据增强用于疾病诊断

Data Augmentation through Pseudolabels in Automatic Region Based  Coronary Artery Segmentation for Disease Diagnosis

Coronary Artery Diseases although preventable are one of the leading cause of
mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has
proved challenging. This study addresses the automation of resource-intensive
and time-consuming process of manually detecting stenotic lesions in coronary
arteries in X-ray coronary angiography images. To overcome this challenge, we
employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for
instance segmentation tasks. Our empirical findings affirm that the proposed
model exhibits commendable performance in identifying stenotic lesions.
Notably, our approach achieves a substantial F1 score of 0.5353 in this
demanding task, underscoring its effectiveness in streamlining this intensive
process.

冠状动脉疾病的自动化检测和图像分析对诊断过程进行了有效的简化，证实了提出的模型在识别狭窄病变方面表现出色。