VesselShot: Few-shot learning for cerebral blood vessel segmentation
This addresses the challenge of limited labeled data for cerebral blood vessel segmentation in medical imaging, though it is incremental as it builds on existing few-shot learning methods.
The paper tackles the problem of cerebrovascular segmentation by proposing VesselShot, a few-shot learning approach that reduces reliance on extensive manual annotation, achieving a mean Dice coefficient of 0.62 on the TubeTK dataset.
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).