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.
Primary angle closure disease (PACD) is a leading cause of permanent vision loss worldwide, so early treatment of patients suffering from symptoms of PACD is crucial to prevent vision loss. Gonioscopy is the current clinical standard for diagnosing PACD. However, gonioscopy is a qualitative subjective assessment method. Thus, there is a need for a quantitative method to diagnose PACD. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality which produces images of anterior structures such as the anterior chamber angle. Adoption of AS-OCT has been slow due to AS-OCT analysis not being standardized and inefficient. Currently, users 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 over 900 images we have developed a system to streamline and standardize AS-OCT analysis. This system will be DICOM compatible to promote standardization of AS-OCT images. This system will be attached to a HIPAA compliant database and will require a secure login to protect patient privacy. We have 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.
Primary angle closure disease (PACD) is a leading cause of permanent vision loss worldwide, so early treatment of patients suffering from symptoms of PACD is crucial to prevent vision loss. Gonioscopy is the current clinical standard for diagnosing PACD. However, gonioscopy is a qualitative subjective assessment method. Thus, there is a need for a quantitative method to diagnose PACD. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality which produces images of anterior structures such as the anterior chamber angle. Adoption of AS-OCT has been slow due to AS-OCT analysis not being standardized and inefficient. Currently, users 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 over 900 images we have developed a system to streamline and standardize AS-OCT analysis. This system will be DICOM compatible to promote standardization of AS-OCT images. This system will be attached to a HIPAA compliant database and will require a secure login to protect patient privacy. We have developed a streamlined approach towards annotating key features in AS-OCT images which will be used to validate the results of an automatic segmentation method. The automatic segmentation method will be integrated into the system to increase the efficiency of analyzing AS-OCT images and eliminate the need to annotate images for clinical diagnosis. These features will be used in the future to classify PACD based on the severity of the angle closure.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.