Utilizing an open-source environment for cognitive-AI in feature detection and quantitative measurements of optical coherence tomography (OCT) images

Anshu Goyal; Seena Pourzand; Sachi Pawooskar-Almeida; M. Wasil Wahi-Anwar; Galo Apolo Aroca; Benjamin Y. Xu; Matthew Brown; Brent J. Liu

doi:10.1117/12.3006942

2 April 2024 Utilizing an open-source environment for cognitive-AI in feature detection and quantitative measurements of optical coherence tomography (OCT) images

Anshu Goyal, Seena Pourzand, Sachi Pawooskar-Almeida, M. Wasil Wahi-Anwar, Galo Apolo Aroca, Benjamin Y. Xu, Matthew Brown, Brent J. Liu

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293105 (2024) https://doi.org/10.1117/12.3006942
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Primary Angle Closure Disease (PACD) is the most common cause of vision impairment worldwide. Early treatment is crucial in preventing vision loss. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality that produces images of anterior structures such as the Anterior Chamber Angle (ACA) and the scleral spur. However, adoption of the AS-OCT modality has been gradual due to AS-OCT analysis not being standardized and inefficient. medical professionals typically must annotate each image by hand using proprietary software and use expert knowledge to diagnose PACD based on the key features annotated. Using an imaging-informatics-based approach on a dataset of almost 1200 images, we have developed a DICOM-compatible system to streamline and standardize AS-OCT analysis, utilizing a HIPAA-compliant database requiring a secure login to protect patient privacy. Previously, we developed a streamlined approach towards annotating key features in AS-OCT images which will be used to validate the results produced by SimpleMind – an open-source software framework supporting deep neural networks with machine learning and automatic parameter tuning. SimpleMind is integrated into the system to increase the efficiency of analyzing AS-OCT images and eliminate the need to annotate images for clinical diagnosis. The goal is to develop a comprehensive and robust hybrid system combining traditional and deep learning image processing methods to detect the scleral spur and estimate a measure of the anterior chamber angle’s degree of openness from AS-OCT images. This paper presents a hybrid method of determining the ACA boundary region to produce an angle measurement that can help indicate PACD.

Conference Presentation

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

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Anshu Goyal, Seena Pourzand, Sachi Pawooskar-Almeida, M. Wasil Wahi-Anwar, Galo Apolo Aroca, Benjamin Y. Xu, Matthew Brown, and Brent J. Liu "Utilizing an open-source environment for cognitive-AI in feature detection and quantitative measurements of optical coherence tomography (OCT) images", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293105 (2 April 2024); https://doi.org/10.1117/12.3006942

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