Learning-based clustering of multi-depth time-domain full-field OCT retinal images

Ahmed Ben Aissa; Shan Suthaharan; Yao Cai; Kate Grieve; Pedro Mecê

doi:10.1117/12.3000326

13 March 2024 Learning-based clustering of multi-depth time-domain full-field OCT retinal images

Ahmed Ben Aissa, Shan Suthaharan, Yao Cai, Kate Grieve, Pedro Mecê

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC128300S (2024) https://doi.org/10.1117/12.3000326
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

This work presents a clustering approach for Time-domain Full-field optical coherence tomography (TD-FFOCT) retinal images.TD-FFOCT is an efficient method for cellular-level analysis of retinal structures, with fast acquisition and wide field-of-view. However, clinical use faces challenges from involuntary axial retinal motion due to breathing, heartbeat, and pulsation. Despite real-time axial motion compensation, achieved precision is around 10µm rms, below the ideal 4µm for an 8µm coherence gate, impacting system robustness and image depth selection. One way to overcome this is to group together images featuring similar retinal structures and acquired at the same depth. We propose a comprehensive clustering approach using learning-based and non-learning-based methods for feature extraction and clustering. Results show that clustering can help mitigate the effects of motion on the acquired image data, improving imaging accuracy and robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ahmed Ben Aissa, Shan Suthaharan, Yao Cai, Kate Grieve, and Pedro Mecê "Learning-based clustering of multi-depth time-domain full-field OCT retinal images", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC128300S (13 March 2024); https://doi.org/10.1117/12.3000326

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