Cerebrovascular diseases (CVDs) remain a leading cause of global disability
and mortality. Digital Subtraction Angiography (DSA) sequences, recognized as
the golden standard for diagnosing CVDs, can clearly visualize the dynamic flow
and reveal pathological conditions within the cerebrovasculature. Therefore,
precise segmentation of cerebral arteries (CAs) and classification between
their main trunks and branches are crucial for physicians to accurately
quantify diseases. However, achieving accurate CA segmentation in DSA sequences
remains a challenging task due to small vessels with low contrast, and
ambiguity between vessels and residual skull structures. Moreover, the lack of
publicly available datasets limits exploration in the field. In this paper, we
introduce a DSA Sequence-based Cerebral Artery segmentation dataset (DSCA), the
first publicly accessible dataset designed specifically for pixel-level
semantic segmentation of CAs. Additionally, we propose DSANet, a
spatio-temporal network for CA segmentation in DSA sequences. Unlike existing
DSA segmentation methods that focus only on a single frame, the proposed DSANet
introduces a separate temporal encoding branch to capture dynamic vessel
details across multiple frames. To enhance small vessel segmentation and
improve vessel connectivity, we design a novel TemporalFormer module to capture
global context and correlations among sequential frames. Furthermore, we
develop a Spatio-Temporal Fusion (STF) module to effectively integrate spatial
and temporal features from the encoder. Extensive experiments demonstrate that
DSANet outperforms other state-of-the-art methods in CA segmentation, achieving
a Dice of 0.9033.

DSA 序列中的脑血管自动分割的研究，通过引入一个专为像素级语义分割设计的公开可访问数据集 DSCA 和一种名为 DSANet 的时空网络方法进行小血管分割，成功改进了诊断脑血管疾病的准确性。

数字减影血管造影序列数据集和脑动脉分割的时空模型

DSCA: A Digital Subtraction Angiography Sequence Dataset and  Spatio-Temporal Model for Cerebral Artery Segmentation

Angiography is widely used to detect, diagnose, and treat cerebrovascular
diseases. While numerous techniques have been proposed to segment the vascular
network from different imaging modalities, deep learning (DL) has emerged as a
promising approach. However, existing DL methods often depend on proprietary
datasets and extensive manual annotation. Moreover, the availability of
pre-trained networks specifically for medical domains and 3D volumes is
limited. To overcome these challenges, we propose a few-shot learning approach
called VesselShot for cerebrovascular segmentation. VesselShot leverages
knowledge from a few annotated support images and mitigates the scarcity of
labeled data and the need for extensive annotation in cerebral blood vessel
segmentation. We evaluated the performance of VesselShot using the publicly
available TubeTK dataset for the segmentation task, achieving a mean Dice
coefficient (DC) of 0.62(0.03).

血管拍片是广泛用于检测、诊断和治疗脑血管疾病的方法。由于现有的深度学习方法往往依赖于专有数据集和大量的手动标注，我们提出了一种名为 VesselShot 的少样本学习方法，通过利用少量有注释的辅助图像的知识，缓解了脑血管分割中标注数据稀缺和大量标注的问题。我们使用公开可用的 TubeTK 数据集对 VesselShot 的性能进行评估，取得了平均 Dice 系数为 0.62 (0.03) 的分割结果。

VesselShot: 脑血管分割的小样本学习

VesselShot: Few-shot learning for cerebral blood vessel segmentation

The Circle of Willis (CoW) is the part of cerebral vasculature responsible
for delivering blood to the brain. Understanding the diverse anatomical
variations and configurations of the CoW is paramount to advance research on
cerebrovascular diseases and refine clinical interventions. However,
comprehensive investigation of less prevalent CoW variations remains
challenging because of the dominance of a few commonly occurring
configurations. We propose a novel generative approach utilising a conditional
latent diffusion model with shape and anatomical guidance to generate realistic
3D CoW segmentations, including different phenotypical variations. Our
conditional latent diffusion model incorporates shape guidance to better
preserve vessel continuity and demonstrates superior performance when compared
to alternative generative models, including conditional variants of 3D GAN and
3D VAE. We observed that our model generated CoW variants that are more
realistic and demonstrate higher visual fidelity than competing approaches with
an FID score 53\% better than the best-performing GAN-based model.

利用条件潜在扩散模型与形状和解剖导向相结合，提出了一种新颖的生成方法，能够生成逼真的三维 Willis 环分割，包括不同的表型变异，性能优于其他生成模型，包括条件 3D GAN 和 3D VAE。