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Proceedings Paper

Fundus Analysis Software Tool (FAST): development of software integrating CAD with the EHR for the longitudinal study of fundus images
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Paper Abstract

In this paper, we present the previous development and deployment of Fundus Analysis Software Tool (FAST) to enable the analysis of different anatomical features and pathologies within fundus images over time, and demonstrate its usefulness with three use cases. First, we utilized FAST to acquire 616 fundus images from a remote clinic in a HIPAAcompliant manner. An ophthalmologist at the clinic then used FAST to annotate 190 fundus images containing exudates at the pixelwise level in a time-efficient manner. In comparison with publicly available datasets, our dataset constitutes the largest pixelwise-labeled collection of images and the first exudate segmentation dataset with eye-matched pairs of images for a given patient. Second, we developed an optic disk CAD segmentation algorithm, where our algorithm achieved a mean intersection over union of 0.930, comparable to the disagreement between ophthalmologist annotations. We deployed this algorithm into FAST, where it segments and flushes the segmentation onto the computer screen while simultaneously filling out specified optic disk fields of a DICOM-SR report on the fundus image. Third, we integrated our software with the open-source EHR framework OpenMRS, where our software can upload both automatic and manual analyses of the fundus to a remote server using HL7 FHIR standard then retrieve historical reports for a patient chronologically. Finally, we discuss our design decisions in developing FAST, particularly those relating to its treatment of DICOM-SR reports based on fundus images and its usage of the FHIR standard and its next steps towards enabling effective analyses of fundus images.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095406 (15 March 2019); doi: 10.1117/12.2512068
Show Author Affiliations
Veda Murthy, The Univ. of Southern California (United States)
Manjula Shankar, Retina Institute of Karnataka (India)
Justin Lin, The Univ. of Southern California (United States)
Sabrina Lieu, The Univ. of Southern California (United States)
Mayur Patel, The Univ. of Southern California (United States)
Heaven Post, The Univ. of Southern California (United States)
Hemanth Murthy, Retina Institute of Karnataka (India)
Brent J. Liu, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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