Angle-closure assessment in anterior segment OCT images via deep learning

Simple item page
dc.contributor.authorHao, Huaying
dc.contributor.authorZhao, Yitian
dc.contributor.authorYan, Qifeng
dc.contributor.authorHigashita, Risa
dc.contributor.authorZhang, Jiong
dc.contributor.authorZhao, Yifan
dc.contributor.authorXu, Yanwu
dc.contributor.authorLi, Fei
dc.contributor.authorZhang, Xiulan
dc.contributor.authorLiu, Jiang
dc.date.accessioned2021-02-17T11:26:58Z
dc.date.available2021-02-17T11:26:58Z
dc.date.issued2021-01-07
dc.description.abstractPrecise 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.en_UK
dc.identifier.citationHao H, Zhao Y, Yan Q, et al., (2021) Angle-closure assessment in anterior segment OCT images via deep learning. Medical Image Analysis, Volume 69, April 2021, Article number 101956en_UK
dc.identifier.issn1361-8415
dc.identifier.urihttps://doi.org/10.1016/j.media.2021.101956
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16357
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep learningen_UK
dc.subjectAS-OCTen_UK
dc.subjectAnterior chamber angleen_UK
dc.subjectGlaucomaen_UK
dc.subjectAngle-closureen_UK
dc.titleAngle-closure assessment in anterior segment OCT images via deep learningen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Angle-closure_assessment_in_anterior_segment_OCT_images-2021.pdf
Size:
3.51 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)