TY - GEN
T1 - SS-FS CSA
T2 - 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
AU - Niu, Chenxi
AU - Liu, Ziyu
AU - He, Xiangjian
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/12/28
Y1 - 2024/12/28
N2 - 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.
AB - 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.
KW - Cerebrovascular segmentation
KW - deep learning
KW - self-supervised learning
KW - TOF-MRA
UR - http://www.scopus.com/inward/record.url?scp=85216234256&partnerID=8YFLogxK
U2 - 10.1145/3696409.3700291
DO - 10.1145/3696409.3700291
M3 - Conference contribution
AN - SCOPUS:85216234256
T3 - Proceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
BT - Proceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
PB - Association for Computing Machinery, Inc
Y2 - 3 December 2024 through 6 December 2024
ER -