Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

Hsia, Wei Ping; Tse, Siu Lun; Chang, Chia Jen; Huang, Yu-Len

doi:10.3390/app11125488

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…The major ndings of this study are outlined as follows: (1) The mean choroidal thickness reduced signi cantly for every 20 For our Mask R-CNN model predictions, the mean choroidal thickness was 198.3 ± 58.4 µm, which was lower than that obtained using physician sketches (229.5 ± 70.6 µm). The error observed for our model (8.56 pixels) is slightly higher than the average error (4.56 pixels) observed for a previously proposed model [15]. We noticed found that the prediction error was higher in the 20-39-year group, with thicker choroids, than in the other two groups.…”

Section: Discussioncontrasting

confidence: 67%

“…Hsia et al proposed a Mask R-CNN model for the automatic segmentation of the choroid. They observed that their model could preserve crucial information on location, including shallow and deep layer features, and that it could achieve an accurate prediction rate and faster choroidal boundary segmentation [15]. Overall, Mask R-CNN models are accurate and effective in the automatic segmentation of choroid images.…”

Section: Introductionmentioning

confidence: 99%

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Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model

et al. 2022

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Propose: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this study, we aimed to investigate choroidal thickness in healthy eyes by utilizing the Mask R-CNN model. Methods: A total of 68 eyes from 57 participants without signi cant ocular disease were recruited. The participants were allocated to one of three groups according to their age and underwent spectral domain optical coherence tomography (SD-OCT) centered on the fovea. Physicians labelled the choroidal contours in all the OCT sequences. We applied the Mask R-CNN model for automatic segmentation.Comparisons of choroidal thicknesses were conducted according to age and prediction accuracy.Results: Older age groups had thinner choroids, according to the automatic segmentation results; the mean choroidal thickness was 253.7 ± 41.9 μm in the youngest group, 206.8 ± 35.4 μm in the middleaged group, and 152.5 ± 45.7 μm in the oldest group (p < 0.01). Measurements obtained using physician sketches demonstrated similar trends. We observed a signi cant negative correlation between choroidal thickness and age (p < 0.01). The prediction error was lower and less variable in choroids that were thinner than the cutoff point of 280 μm. Conclusion:The mean choroidal thickness decreased with age in healthy subjects. The Mask R-CNN model can accurately predict choroidal thickness, especially choroids thinner than 280 μm. This model can enable exploring larger and more varied choroid datasets comprehensively, automatically, and conveniently.

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Section: Discussioncontrasting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model

et al. 2022

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“…S2 , n = 6, standard deviation = 4.69 µm). Similar delineation approaches were reported before for retinal 30 – 33 and choroidal 34 – 36 segmentation from OCT images. We have measured the mean PChT from each pixel location across the exposed horizontal choroidal area with over 240 axial measurements.…”

Section: Methodssupporting

confidence: 56%

Peripheral Choroidal Response to Localized Defocus Blur: Influence of Native Peripheral Aberrations

Pusti,

Patel,

Ostrin

et al. 2024

Invest. Ophthalmol. Vis. Sci.

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Purpose This study aims to examine the short-term peripheral choroidal thickness (PChT) response to signed defocus blur, both with and without native peripheral aberrations. This examination will provide insights into the role of peripheral aberration in detecting signs of defocus. Methods The peripheral retina (temporal 15°) of the right eye was exposed to a localized video stimulus in 11 young adults. An adaptive optics system induced 2D myopic or hyperopic defocus onto the stimulus, with or without correcting native peripheral ocular aberrations (adaptive optics [AO] or NoAO defocus conditions). Choroidal scans were captured using Heidelberg Spectralis OCT at baseline, exposure (10, 20, and 30 minutes), and recovery phases (4, 8, and 15 minutes). Neural network-based automated MATLAB segmentation program measured PChT changes from OCT scans, and statistical analysis evaluated the effects of different optical conditions over time. Results During the exposure phase, NoAO myopic and hyperopic defocus conditions exhibited distinct bidirectional PChT alterations, showing average thickening (10.0 ± 5.3 µm) and thinning (−9.1 ± 5.5 µm), respectively. In contrast, induced AO defocus conditions did not demonstrate a significant change from baseline. PChT recovery to baseline occurred for all conditions. The unexposed fovea did not show any significant ChT change, indicating a localized ChT response to retinal blur. Conclusions We discovered that the PChT response serves as a marker for detecting peripheral retinal myopic and hyperopic defocus blur, especially in the presence of peripheral aberrations. These findings highlight the significant role of peripheral oriented blur in cueing peripheral defocus sign detection.

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“…In the deep learning side of RLS, [20,21] respectively used Mask R-CNN and a patch-based classifier to segment choroidal boundaries whereas, [22] used a modified Xception network, and [23] adopted a deep feature-learning regression network to detect retinal contours. [24] proposed a combined CNN and LSTM based networks for pre-processing OCT images.…”

Section: Related Workmentioning

confidence: 99%

Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In Oct Images

Bhattarai

Kambhamettu

Jin³

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Recent deep learning-based contour detection studies show high accuracy in single-class boundary detection problems. However, this performance does not translate well in a multi-class scenario where continuous contours are required. Our research presents CU-Net, a U-Net-based network with residual-net encoders which can produce accurate and uninterrupted contour lines for multiple classes. The critical factor behind this concept is our continuity module, containing an interpolation layer and a novel activation function that converts discrete signals into smooth contours. We find the application of our approach in medical imaging problems like retinal layer segmentation from optical coherence tomography (OCT) scans. We applied our method to an expert annotated OCT dataset of children with sickle-cell disease. To compare with benchmarks, we evaluated our network on DME and HC-MS datasets. We achieved an overall mean absolute distance of 6.48 ± 2.04µM and 1.97 ± 0.89µM , respectively 1.03 and 1.4 times less than the current state-of-the-art.

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Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

Cited by 16 publications

References 39 publications

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model

Peripheral Choroidal Response to Localized Defocus Blur: Influence of Native Peripheral Aberrations

Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In Oct Images

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