Jianfei Liu1, HaeWon Jung1, Alfredo Dubra2, Johnny Tam1. 1. Ophthalmic Genetics and Visual Function Branch, National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States. 2. Department of Ophthalmology, Stanford University, Palo Alto, California, United States.
Abstract
Purpose: Cone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics. Methods: Cone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data. Results: Overall, there was good agreement between automated and manual segmentations and between diameter measurements (n = 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05). Conclusions: CCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.
Purpose: Cone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics. Methods: Cone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data. Results: Overall, there was good agreement between automated and manual segmentations and between diameter measurements (n = 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05). Conclusions: CCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.
Authors: Jacque L Duncan; Yuhua Zhang; Jarel Gandhi; Chiaki Nakanishi; Mohammad Othman; Kari E H Branham; Anand Swaroop; Austin Roorda Journal: Invest Ophthalmol Vis Sci Date: 2007-07 Impact factor: 4.799
Authors: HaeWon Jung; Jianfei Liu; Tao Liu; Aman George; Margery G Smelkinson; Sarah Cohen; Ruchi Sharma; Owen Schwartz; Arvydas Maminishkis; Kapil Bharti; Catherine Cukras; Laryssa A Huryn; Brian P Brooks; Robert Fariss; Johnny Tam Journal: JCI Insight Date: 2019-03-21
Authors: Jianfei Liu; Christine Shen; Nancy Aguilera; Catherine Cukras; Robert B Hufnagel; Wadih M Zein; Tao Liu; Johnny Tam Journal: IEEE Trans Med Imaging Date: 2021-09-30 Impact factor: 11.037