eprintid: 10178518
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/17/85/18
datestamp: 2023-10-10 08:41:37
lastmod: 2023-10-10 10:07:45
status_changed: 2023-10-10 08:41:37
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Chen, Boyu
creators_name: Solebo, Ameenat L
creators_name: Taylor, Paul
title: Automated Image Quality Assessment for Anterior Segment Optical Coherence Tomograph
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D10
divisions: DD4
divisions: D13
divisions: G08
divisions: G25
keywords: Q-factor; Image quality; Deep learning; Image segmentation; Biomedical optical imaging;
Optical coherence tomography; Coherence
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Optical Coherence Tomography (OCT) is a technique for diagnosing eye disorders. Image quality assessment (IQA) of OCT images is essential, but manual IQA is time consuming and subjective. Recently, automated IQA methods based on deep learning (DL) have achieved good performance. However, few of these methods focus on OCT images of the anterior segment of the eye (AS-OCT). Moreover, few of these methods identify the factors that affect the quality of the images (called "quality factors" in this paper). This could adversely affect the acceptance of their results. In this study, we define, for the first time to the best of our knowledge, the quality level and four quality factors of AS-OCT for the clinical context of anterior chamber inflammation. We also develop an automated framework based on multi-task learning to assess the quality and to identify the existing of quality factors in the AS-OCT images. The effectiveness of the framework is demonstrated in experiments.
date: 2023-09-01
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ISBI53787.2023.10230756
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2094790
doi: 10.1109/ISBI53787.2023.10230756
isbn_13: 978-1-6654-7358-3
lyricists_name: Solebo, Ameenat
lyricists_name: Taylor, Peter
lyricists_name: Chen, Boyu
lyricists_id: OASOL02
lyricists_id: PTAYL42
lyricists_id: BCHEB09
actors_name: Chen, Boyu
actors_id: BCHEB09
actors_role: owner
full_text_status: public
pres_type: paper
series: International Symposium on Biomedical Imaging (ISBI)
publication: Proceedings - International Symposium on Biomedical Imaging
volume: 20
place_of_pub: Cartagena, Colombia
event_title: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
event_dates: 18 Apr 2023 - 21 Apr 2023
issn: 1945-8452
book_title: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
citation:        Chen, Boyu;    Solebo, Ameenat L;    Taylor, Paul;      (2023)    Automated Image Quality Assessment for Anterior Segment Optical Coherence Tomograph.                     In:  2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI).    IEEE: Cartagena, Colombia.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10178518/1/ISBI23_paper_057.pdf