TY - GEN
T1 - Automated Computational Diagnosis of Peripheral Retinal Pathology in Optical Coherence Tomography (OCT) Scans using Graph Theory
AU - Lange, Tyra
AU - Lake, Stewart
AU - Reynolds, Karen
AU - Bottema, Murk
PY - 2020/11/29
Y1 - 2020/11/29
N2 - Analysis of retinal shape with optical coherence tomography (OCT) has been valuable in describing different ophthalmic conditions. An effective method for retinal contour delineation is graph theory. This study compares the ability of two different implementations of graph theory, the Livewire (LVW) intelligent scissors developed for ImageJ and a purpose-built graph searching function (GSF), to determine retinal shape for a retinal disease classifier. Both methods require user interaction. Retinal shape features derived from both methods were used to diagnose eyes with posterior vitreous detachment (PVD) or retinal detachment (RD) via quadratic discriminant analysis. Classification with each method was the same in 49 out of 51 eyes. Processing time was faster with the GSF than LVW. In mean (µ) ± standard deviation (SD), GSF took 524 ± 62 s and LVW took 814 ± 223 s (p = 5.52 x 10-14). Conclusively, GSF was easier to use and is preferred for further retinal shape analysis.
AB - Analysis of retinal shape with optical coherence tomography (OCT) has been valuable in describing different ophthalmic conditions. An effective method for retinal contour delineation is graph theory. This study compares the ability of two different implementations of graph theory, the Livewire (LVW) intelligent scissors developed for ImageJ and a purpose-built graph searching function (GSF), to determine retinal shape for a retinal disease classifier. Both methods require user interaction. Retinal shape features derived from both methods were used to diagnose eyes with posterior vitreous detachment (PVD) or retinal detachment (RD) via quadratic discriminant analysis. Classification with each method was the same in 49 out of 51 eyes. Processing time was faster with the GSF than LVW. In mean (µ) ± standard deviation (SD), GSF took 524 ± 62 s and LVW took 814 ± 223 s (p = 5.52 x 10-14). Conclusively, GSF was easier to use and is preferred for further retinal shape analysis.
KW - computational diagnosis
KW - graph theory
KW - Optical coherence tomography (OCT)
KW - retinal pathology
UR - http://www.scopus.com/inward/record.url?scp=85102617512&partnerID=8YFLogxK
U2 - 10.1109/DICTA51227.2020.9363376
DO - 10.1109/DICTA51227.2020.9363376
M3 - Conference contribution
AN - SCOPUS:85102617512
SN - 9781728191096
T3 - 2020 Digital Image Computing: Techniques and Applications, DICTA 2020
BT - 2020 Digital Image Computing
PB - Institute of Electrical and Electronics Engineers
CY - Melbourne, Australia
T2 - 2020 Digital Image Computing: Techniques and Applications, DICTA 2020
Y2 - 29 November 2020 through 2 December 2020
ER -