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

This study explores the use of Federated Learning (FL) for stenosis detection
in coronary angiography images (CA). Two heterogeneous datasets from two
institutions were considered: Dataset 1 includes 1219 images from 200 patients,
which we acquired at the Ospedale Riuniti of Ancona (Italy); Dataset 2 includes
7492 sequential images from 90 patients from a previous study available in the
literature. Stenosis detection was performed by using a Faster R-CNN model. In
our FL framework, only the weights of the model backbone were shared among the
two client institutions, using Federated Averaging (FedAvg) for weight
aggregation. We assessed the performance of stenosis detection using Precision
(P rec), Recall (Rec), and F1 score (F1). Our results showed that the FL
framework does not substantially affects clients 2 performance, which already
achieved good performance with local training; for client 1, instead, FL
framework increases the performance with respect to local model of +3.76%,
+17.21% and +10.80%, respectively, reaching P rec = 73.56, Rec = 67.01 and F1 =
70.13. With such results, we showed that FL may enable multicentric studies
relevant to automatic stenosis detection in CA by addressing data heterogeneity
from various institutions, while preserving patient privacy.

本研究探讨了联邦学习在冠状动脉造影图像（CA）中用于狭窄检测的应用。研究使用两个异构数据集，分别来自意大利 Ancona 市的 Ospedale Riuniti 医院和前期研究的文献中的成像数据。使用 Faster R-CNN 模型进行狭窄检测，并通过联邦平均（FedAvg）对模型背骨权重进行共享。结果显示，联邦学习框架对第二客户端的性能影响不大，而对第一客户端，与本地模型相比，性能提升了分别为 + 3.76％，+17.21％和 + 10.80％，P_rec 为 73.56，Rec 为 67.01，F1 为 70.13。这些结果表明，联邦学习可实现不同机构之间相关的自动狭窄检测的多中心研究，同时保护患者隐私。