VesselShot：面向脑血管分割的小样本学习
VesselShot: Few-shot learning for cerebral blood vessel segmentation
AI总结：
针对脑血管分割中深度学习方法依赖大量标注、医学预训练网络有限的问题，提出小样本学习方法VesselShot，利用少量支持图像知识缓解标注稀缺问题，在TubeTK数据集上取得0.62(0.03)的平均戴斯系数。
摘要：
血管造影术被广泛用于脑血管疾病的检测、诊断与治疗。尽管已有大量技术可从不同成像模态中分割血管网络，深度学习（DL）已成为一种颇具前景的方法。但现有深度学习方法往往依赖专有数据集和大量人工标注，且专门针对医学领域与三维体数据的预训练网络十分有限。为应对这些挑战，我们提出一种名为VesselShot的小样本学习方法用于脑血管分割。VesselShot利用少量带标注支持图像的知识，缓解了脑血管分割中标注数据稀缺、需大量标注的问题。我们使用公开的TubeTK数据集评估了VesselShot的分割性能，其平均戴斯系数（DC）达到0.62(0.03)。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
