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).

翻译：血管造影术广泛应用于脑血管疾病的检测、诊断和治疗。尽管已有多种技术被提出用于从不同成像模态中分割血管网络，但深度学习（DL）已成为一种有前景的方法。然而，现有的深度学习方法往往依赖专有数据集和大量的人工标注。此外，针对医学领域和三维体数据的预训练网络可用性有限。为克服这些挑战，我们提出一种名为VesselShot的少样本学习方法，用于脑血管分割。VesselShot利用少量标注的支持图像中获取的知识，缓解了脑部血管分割中标注数据稀缺及需大量人工标注的问题。我们使用公开的TubeTK数据集评估了VesselShot在分割任务中的性能，实现了平均Dice系数（DC）为0.62（±0.03）。