Browsing by Author "Zhang, Jiong"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access 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, JiangPrecise 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.Item Open Access 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, YonghuaiPrecise 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.Item Open Access 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, BaihuaCurrently 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