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SS-FS CSA: Self-Supervised and Fully Supervised Integration for 3D Cerebrovascular Segmentation

Published: 28 December 2024 Publication History

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.

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  1. SS-FS CSA: Self-Supervised and Fully Supervised Integration for 3D Cerebrovascular Segmentation

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    cover image ACM Conferences
    MMAsia '24: Proceedings of the 6th ACM International Conference on Multimedia in Asia
    December 2024
    939 pages
    ISBN:9798400712739
    DOI:10.1145/3696409
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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    Published: 28 December 2024

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    Author Tags

    1. Cerebrovascular segmentation
    2. deep learning
    3. self-supervised learning
    4. TOF-MRA

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    MMAsia '24: ACM Multimedia Asia
    December 3 - 6, 2024
    Auckland, New Zealand

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    Overall Acceptance Rate 59 of 204 submissions, 29%

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