Browsing by Author "Frangi, Alejandro F."

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    COSTA: a multi-center TOF-MRA dataset and a style self-consistency network for cerebrovascular segmentation
    (IEEE, 2024-12) Mou, Lei; Yan, Qifeng; Lin, Jinghui; Zhao, Yifan; Liu, Yonghuai; Ma, Shaodong; Zhang, Jiong; Lv, Wenhao; Zhou, Tao; Frangi, Alejandro F.; Zhao, Yitian
    Time-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.
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    Early detection of dementia through retinal imaging and trustworthy AI
    (Springer , 2024-10-04) Hao, Jinkui; Kwapong, William R.; Shen, Ting; Fu, Huazhu; Xu, Yanwu; Lu, Qinkang; Liu, Shouyue; Zhang, Jiong; Liu, Yonghuai; Zhao, Yifan; Zheng, Yalin; Frangi, Alejandro F.; Zhang, Shuting; Qi, Hong; Zhao, Yitian
    Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection.