Automated detection of choroid boundary and vessels in optical coherence tomography images

Nizampatnam, Srinath; Patil, Ameya; Kumar, Vipin; Jana, Soumya; Chhablani, Jay; Richhariya, Ashutosh

doi:10.1109/embc.2014.6943555

Cited by 12 publications

(8 citation statements)

References 17 publications

(12 reference statements)

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“…Srinath et al used structural similarity (SSIM), adaptive Hessian analysis and level set technique in order to automatic segmentation of choroid. Experimental results were presented using SDOCT images and showed that the proposed method hugs choroid vessels while the manual segmentations were smooth curve below them [58].…”

Section: Automatic Segmentation Methods Of Choroidal Thickness From Oct Imagesmentioning

confidence: 99%

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

et al. 2022

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Choroid is one of the structural layers, playing a significant role in physiology of the eye and lying between the sclera and the retina. The segmentation of this layer could guide ophthalmologists in diagnosing most of the eye pathologies such as choroidal tumors and polypoidal choroidal vasculopathy. High signal-to-noise ratio and high speed imaging in Spectral-Domain Optical Coherence Tomography (SD-OCT) make choroidal imaging feasible. Several variables such as pre-operative axial length (AXL), time of day and age affect thickness of the choroidal vascularization and should be considered for segmentation of this layer. These days most of the eye specialists manually segment the choroidal layer which is time-consuming, tiresome and dependent on human errors. To overcome these difficulties, some studies have introduced different automatic choroidal segmentation methods. In this paper, we have conducted a comprehensive review on existing recently published methods for automatic choroidal segmentation algorithms.

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Section: Automatic Segmentation Methods Of Choroidal Thickness From Oct Imagesmentioning

confidence: 99%

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

et al. 2022

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“…We performed the following experiments: (a) evaluation of the segmentation accuracy of the RefineNet method compared to manual ground truth (from observers); (b) comparison of inter-observer variability; and (c) comparison of our RefineNet with other choroidal vessels segmentation methods. These comparison methods are: (1) VGG-FCN -traditional fully convolutional network based on VGGNet architecture [11]; (2) LS -level set based segmentation method, which was used in [10]; and (3) AT -adaptive thresholding based segmentation method, which was used in [7].…”

Section: Methodsmentioning

confidence: 99%

“…In their experiments, only the reproducibility was demonstrated using repeated scans of the same eye while segmentation accuracy was not reported. In another work, Srinath et al [10] proposed to initially identify the upper and lower boundaries of the choroid layer and then apply a level set method on the detected choroid layer to iteratively segment the vessels. By isolating the choroid layer, they were able to improve the identification of the vessels and its subsequent segmentation.…”

Section: Related Workmentioning

confidence: 99%

Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images

Liu

et al. 2019

Biomed. Opt. Express

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Accurate choroidal vessel segmentation with swept-source optical coherence tomography (SS-OCT) images provide unprecedented quantitative analysis towards the understanding of choroid-related diseases. Motivated by the leading segmentation performance in medical images from the use of deep learning methods, in this study, we proposed the adoption of a deep learning method, RefineNet, to segment the choroidal vessels from SS-OCT images. We quantitatively evaluated the RefineNet on 40 SS-OCT images consisting of ~3,900 manually annotated choroidal vessels regions. We achieved a segmentation agreement (SA) of 0.840 ± 0.035 with clinician 1 (C1) and 0.823 ± 0.027 with clinician 2 (C2). These results were higher than inter-observer variability measure in SA between C1 and C2 of 0.821 ± 0.037. Our results demonstrated that the choroidal vessels from SS-OCT can be automatically segmented using a deep learning method and thus provided a new approach towards an objective and reproducible quantitative analysis of vessel regions.

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“…Finally, we investigate our hypothesis on the importance of choroid quality assessment in OCT images by considering a practical scenario. Specifically, we consider an automated tool for detecting choroidal inner boundary (CIB) and choroid outer boundary (COB) 42 , a primary step in screening or quantification of chorioretinal diseases. As a pilot study, we randomly selected few OCT images from both good and bad quality of our dataset, and obtained manual annotations of choroid boundaries by expert.…”

Section: Visual Explanationsmentioning

confidence: 99%

Deep Learning Based Diagnostic Quality Assessment of Choroidal OCT Features with Expert-Evaluated Explainability

Koidala

Manne

Ozimba

et al. 2022

Preprint

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Various vision-threatening eye diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy(CSCR) are caused due to the dysfunctions manifested in the highly vascular choroid layer of the posterior segment of the eye.In the current clinical practice, screening choroidal structural changes is widely based on optical coherence tomography (OCT)images. Accordingly, to assist clinicians, several automated choroidal biomarker detection methods using OCT images aredeveloped. However, the performance of these algorithms is largely constrained by the quality of the OCT scan. Consequently,determining the quality of choroidal features in OCT scans is significant in building standardized quantification tools and henceconstitutes our main objective. This study includes a dataset of 1593 good and 2581 bad quality Spectralis OCT images gradedby an expert. Noting the efficacy of deep-learning (DL) in medical image analysis, we propose to train three state-of-the-artDL models: ResNet18, EfficientNet-B0 and EfficientNet-B3 to detect the quality of OCT images. The choice of these modelswas inspired by their ability to preserve the salient features across all the layers without information loss. To evaluate theattention of DL models on the choroid, we introduced color transparency maps (CTMs) based on GradCAM explanations.Further, we proposed two subjective grading scores: overall choroid coverage (OCC) and choroid coverage in the visibleregion(CCVR) based on CTMs to objectively correlate visual explanations vis-à-vis DL model attentions. We observed that theaverage accuracy and F-scores for the three DL models are greater than 96%. Further, the OCC and CCVR scores achievedfor the three DL models under consideration substantiate that they mostly focus on the choroid layer in making the decision. Inparticular, of the three DL models, EfficientNet-B3 is in close agreement with the clinician’s inference. The proposed DL-basedframework demonstrated high detection accuracy as well as attention on the choroid layer, where EfficientNet-B3 reportedsuperior performance. Our work assumes significance in bench-marking the automated choroid biomarker detection tools andfacilitating high-throughput screening. Further, the methods proposed in this work can be adopted for evaluating the attentionof DL-based approaches developed for other region-specific quality assessment tasks.

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Automated detection of choroid boundary and vessels in optical coherence tomography images

Cited by 12 publications

References 17 publications

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images

Deep Learning Based Diagnostic Quality Assessment of Choroidal OCT Features with Expert-Evaluated Explainability

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