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