Automatic segmentation of bladder layers in Optical Coherence Tomography images using graph theory and dynamic programming

Abstract Background: Automatic segmentation of the choroid on Optical Coherence Tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. In nature, it is not as exhausting as manual segmentation and does not depend on human errors. In this study, sixty EDI-OCT (Enhanced Depth Imaging Optical Coherence Tomography) images of both normal and abnormal eyes gathered from Isfahan Feiz Medical Center were used. The data were manually segmented by a retinal ophthalmologist to draw comparison with the proposed automatic segmentation technique.Methods: In this study, curvelet transform based KSVD dictionary learning and Lucy-Richardson algorithm was used to remove speckle noise from OCT images. The Outer / Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and OCB was considered as choroidal region.Results: The method was evaluated on the EDI-OCT images and the average Dice Similarity Coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al on our dataset the mean DSC was calculated to be 55.75% ± 14.54%. Conclusions: A significant similarity was observed between automatic and manual segmentations, in both normal and abnormal eyes. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.

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

Applied Sciences ◽

10.3390/app11125488 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5488

Author(s):

Wei Ping Hsia ◽

Siu Lun Tse ◽

Chia Jen Chang ◽

Yu Len Huang

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Choroidal Thickness ◽

Automatic Segmentation ◽

Average Error ◽

Good Prediction ◽

Optical Coherence ◽

Learning Method ◽

Subfoveal Choroidal Thickness ◽

Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

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Automatic segmentation of retinal edema in optical coherence tomography based on deep neural networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1861/1/012056 ◽

2021 ◽

Vol 1861 (1) ◽

pp. 012056

Author(s):

Rongrong Lu ◽

Lidan Wang ◽

Shukai Duan

Keyword(s):

Neural Networks ◽

Optical Coherence Tomography ◽

Deep Neural Networks ◽

Automatic Segmentation ◽

Optical Coherence ◽

Retinal Edema

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High-speed automatic segmentation of intravascular stent struts in optical coherence tomography images

10.1117/12.2004335 ◽

2013 ◽

Cited By ~ 1

Author(s):

M. Han ◽

D. Kim ◽

W. Y. Oh ◽

S. Ryu

Keyword(s):

Optical Coherence Tomography ◽

High Speed ◽

Automatic Segmentation ◽

Optical Coherence ◽

Intravascular Stent

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Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545822420019 ◽

2021 ◽

Author(s):

Jian Liu ◽

Shixin Yan ◽

Nan Lu ◽

Dongni Yang ◽

Chunhui Fan ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Healthy Subjects ◽

Optical Coherence Tomography Angiography ◽

Automatic Segmentation ◽

Qualitative Evaluation ◽

Foveal Avascular Zone ◽

Watershed Algorithm ◽

Strong Positive Correlation ◽

Optical Coherence ◽

Avascular Zone

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, “over-segmentation” is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant “dams”. This paper analyzed the relationship between the “dams” length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of “over-segmentation”. Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of “perfect segmentation” (score of 3) and “good segmentation” (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

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