TY  - GEN
AV  - public
CY  - Cartagena, Colombia
UR  - https://doi.org/10.1109/ISBI53787.2023.10230756
SN  - 1945-8452
A1  - Chen, Boyu
A1  - Solebo, Ameenat L
A1  - Taylor, Paul
N2  - 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.
KW  - Q-factor; Image quality; Deep learning; Image segmentation; Biomedical optical imaging;
Optical coherence tomography; Coherence
T3  - International Symposium on Biomedical Imaging (ISBI)
PB  - IEEE
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10178518
Y1  - 2023/09/01/
TI  - Automated Image Quality Assessment for Anterior Segment Optical Coherence Tomograph
ER  -