COSTA: a multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation

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dc.contributor.authorMou, Lei
dc.contributor.authorYan, Qifeng
dc.contributor.authorLin, Jinghui
dc.contributor.authorZhao, Yifan
dc.contributor.authorLiu, Yonghuai
dc.contributor.authorMa, Shaodong
dc.contributor.authorZhang, Jiong
dc.contributor.authorLv, Wenhao
dc.contributor.authorZhou, Tao
dc.contributor.authorFrangi, Alejandro F.
dc.contributor.authorZhao, Yitian
dc.date.accessioned2024-08-08T14:51:02Z
dc.date.available2024-08-08T14:51:02Z
dc.date.freetoread2024-08-08
dc.date.issued2024-07-16
dc.description.abstractTime-of-flight magnetic resonance angiography (TOF-MRA) is the least invasive and ionizing radiation-free approach for cerebrovascular imaging, but variations in imaging artifacts across different clinical centers and imaging vendors result in inter-site and inter-vendor heterogeneity, making its accurate and robust cerebrovascular segmentation challenging. Moreover, the limited availability and quality of annotated data pose further challenges for segmentation methods to generalize well to unseen datasets. In this paper, we construct the largest and most diverse TOF-MRA dataset (COSTA) from 8 individual imaging centers, with all the volumes manually annotated. Then we propose a novel network for cerebrovascular segmentation, namely CESAR, with the ability to tackle feature granularity and image style heterogeneity issues. Specifically, a coarse-to-fine architecture is implemented to refine cerebrovascular segmentation in an iterative manner. An automatic feature selection module is proposed to selectively fuse global long-range dependencies and local contextual information of cerebrovascular structures. A style self-consistency loss is then introduced to explicitly align diverse styles of TOF-MRA images to a standardized one. Extensive experimental results on the COSTA dataset demonstrate the effectiveness of our CESAR network against state-of-the-art methods. We have made 6 subsets of COSTA with the source code online available, in order to promote relevant research in the community.
dc.description.journalNameIEEE Transactions on Medical Imaging
dc.identifier.citationMou L, Yan Q, Lin J, et al., (2024) COSTA: a multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation. IEEE transactions on medical imaging, Available online 16 July 2024
dc.identifier.eissn1558-254X
dc.identifier.issn0278-0062
dc.identifier.urihttps://doi.org/10.1109/TMI.2024.3424976
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22759
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10599360
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMulti-center and multi-vector
dc.subjectTOF-MRA
dc.subjectheterogeneity
dc.subjectstyle self-consistency
dc.subjectcerebrovascular segmentation
dc.titleCOSTA: a multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation
dc.typeArticle

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