SS-FS CSA: Self-Supervised and Fully Supervised Integration for 3D Cerebrovascular Segmentation
Year: 2024
Type: conference-paper
Abstract: Three-dimensional cerebrovascular segmentation is crucial for accurate diagnosis and treatment planning of cerebrovascular diseases.However, the lack of high-quality publicly labelled datasets can limit sufficient training, leading to inaccurate results.To address this issue, this study proposes a novel method that combines self-supervised and fully supervised learning, termed the SS-FS Cerebrovascular Segmentation Approach (SS-FS CSA).The method introduces publicly available unlabelled databases into the training process, alleviating the problem of insufficient high-quality labelled medical datasets.The SS-FS CSA method achieves a Dice Similarity Coefficient (DSC) of 82.82%, improving over 2% compared to the SOTA baseline, proving its validity and feasibility in 3D segmentation tasks.
Authors: Chenxi Niu, Ziyu Liu, Xiangjian He
Institution: University of Nottingham Ningbo China
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405867860