Browsing by Author "Zhang, Jiong"

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    Angle-closure assessment in anterior segment OCT images via deep learning
    (Elsevier, 2021-01-07) Hao, Huaying; Zhao, Yitian; Yan, Qifeng; Higashita, Risa; Zhang, Jiong; Zhao, Yifan; Xu, Yanwu; Li, Fei; Zhang, Xiulan; Liu, Jiang
    Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients’ eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy.
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    Automated tortuosity analysis of nerve fibers in corneal confocal microscopy
    (IEEE, 2020-02-17) Zhao, Yitian; Zhang, Jiong; Pereira, Ella; Zheng, Yalin; Su, Pan; Xie, Jianyang; Zhao, Yifan; Shi, Yonggang; Qi, Hong; Liu, Jiang; Liu, Yonghuai
    Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification. The image enhancement component is based on an extended Retinex model, which not only corrects imbalanced illumination and improves image contrast in an image, but also models noise explicitly to aid removal of imaging noise. Afterwards, we take advantage of exponential curvature estimation in the 3D space of positions and orientations to directly measure curvature based on the enhanced images, rather than relying on the explicit segmentation and skeletonization steps in a conventional pipeline usually with accumulated pre-processing errors. The proposed method has been applied over two corneal nerve microscopy datasets for the estimation of a tortuosity level for each image. The experimental results show that it performs better than several selected state-of-the-art methods. Furthermore, we have performed manual gradings at tortuosity level of four hundred and three corneal nerve microscopic images, and this dataset has been released for public access to facilitate other researchers in the community in carrying out further research on the same and related topics.
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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.
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    A rapid and high-throughput quantum dots bioassay for monitoring of perfluorooctane sulfonate in environmental water samples
    (Elsevier Science B.V., Amsterdam., 2011-12-31T00:00:00Z) Zhang, Jiong; Wan, Yanjian; Li, Yuanyuan; Zhang, Qiongfang; Xu, Shunqing; Zhu, Huijun; Shu, Baihua
    Currently HPLC/MS is the state of the art tool for environmental/drinking water perfluorooctane sulfonate (PFOS) monitoring. PFOS can bind to peroxisomal proliferator-activated receptor-alpha (PPARα), which forms heterodimers with retinoid X receptors (RXRs) and binds to PPAR response elements. In this bioassay free PFOS in water samples competes with immobilized PFOS in ELISA plates for a given amount of PPARα-RXRα. It can be determined indirectly by immobilizing PPARα-RXRα-PFOS complex to another plate coated with PPARα antibody and subsequent measuring the level of PPARα-RXRα by using biotin-modified PPARα- RXRα probes-quantum dots-streptavidin detection system. The rapid and high- throughput bioassay demonstrated a detection limit of 2.5ngL-1 with linear range between 2.5ngL-1 and 75ngL-1. Detection results of environmental water samples were highly consistent between the bioassay an