3D graph-based automated segmentation of corneal layers in anterior-segment optical coherence tomography images of mice

Robles, Victor A.; Antony, Bhavna J.; Koehn, Demelza; Anderson, Michael G.; Garvin, Mona K.

doi:10.1117/12.2043523

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…Their results had good agreement with manual observers only for the central region of the cornea where the highest signal to noise ratio was found. A 3D approach is also proposed by Robles et al [ 28 ] to segment three main corneal boundaries by graph-based method. In a more recent work by Williams et al [ 29 ], a Graph Cut based segmentation technique is proposed to improve the speed and efficiency of the segmentation.…”

Section: Introductionmentioning

confidence: 99%

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation

Rabbani

Kafieh

Jahromi

et al. 2016

International Journal of Biomedical Imaging

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Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy.

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Section: Introductionmentioning

confidence: 99%

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation

Rabbani

Kafieh

Jahromi

et al. 2016

International Journal of Biomedical Imaging

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“…Hence, ex-perts have sought to develop automatic alternatives. In the case of using AS-OCT imaging modalities to automatically segment corneal layers [12][13][14][15][16][17][18][19][20], only our previous works [15] have utilized using the information from the entire volume to segment multiple layers simultaneously and in 3D. Additionally, while methodologies exist to segment the corneal fiber layers in IVCM images [21][22][23][24][25][26][27] very little exists for creating a montage of such images [28][29][30] and to the best of our knowledge, only one other work exists that combines segmentation and registration techniques to segment montage images [31].…”

Section: 6mentioning

confidence: 99%

“…Due to the very nature of coherent imaging, the light source will have a tougher time While the basis for some works in the field of corneal layer segmentation sprout from initial work on intraretinal segmentation [62,69,70] only an additional few methods to segment the corneal layers have been developed. A graph-theoretic approach is the most common [14,15,17] while other methods include a variation on the Hough Transform [13], active contours [12], or Gaussian Mixture Models [16] to segment the corneal layers.…”

Section: Previous Workmentioning

confidence: 99%

“…Of all the works currently introduced to segment the corneal layers some are semiautomated [19,20] and some are fully automated [12][13][14][15][16][17]71]. Additionally, many of the fully automated methods [12][13][14]16,17,71] are limited in their scope as they work strictly in 2D, segment only a few of the layers, and/or segment each of the layers individually.…”

Section: Previous Workmentioning

confidence: 99%

“…Additionally, many of the fully automated methods [12][13][14]16,17,71] are limited in their scope as they work strictly in 2D, segment only a few of the layers, and/or segment each of the layers individually. To the best of our knowledge only the foundations of our methodologies published in [15] and our work discussed in Ch. 4 have accomplished segmentation of all the layers simultaneously in 3D.…”

Section: Previous Workmentioning

confidence: 99%

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Automated image analysis of corneal structures in anterior-segment optical coherence tomography and in-vivo confocal microscopy images

Robles¹

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Anterior-Segment-Optical Coherence Tomography (AS-OCT) is a noninvasive imaging modality that has significantly contributed to the quantitative assessment of ocular diseases. Another tool available to ophthalmic clinicians is in-vivo confocal microscopy (IVCM), which allows anatomical structures to be observed live at the cellular level. Incorporating both of these modalities for imaging the cornea allows us to take structural measurements and characterize disease-related changes in corneal anatomy. Notable diseases that directly impact or correlate with corneal structures include glaucoma and diabetic neuropathy. Given glaucoma's impact as the second leading cause of blindness in the world, great efforts have been made in researching and understanding the disease. Diabetes is a prominent disease that affects millions of Americans every day. While not necessarily a corneal disease in its own right, diabetes has been shown to affect the corneal structures. Diabetics have decreased corneal sensitivity and a significant link has been established between neuropathic severity in diabetic patients and corneal nerve fiber density. Given the availability of these imaging tools and the significant impact these prominent diseases have on society a growing focus has developed on relating corneal structure measurements and ophthalmic diseases. However, manually acquiring structural measures of the cornea can be a labor-intensive and daunting task. Hence, experts have sought to develop automatic alternatives. The goals of this work include 1) utilizing the 3D information of AS-OCT imagery to segment all the corneal layers simultaneously, 2) increasing the region-of-interest of IVCM imagery using a feature-based registration approach to develop a panorama from the images, 3) incorporating machine-learning techniques to segment the corneal nerves in the IVCM imagery, and 4) extracting structural measurements from the segmentation results to determine correlation between the corneal structural measurements in various subject groups. vi

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A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

Sleman¹,

Soliman²,

Elsharkawy³

et al. 2021

Medical Physics

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Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data. Methods: To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula midarea) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented. Results: This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches. Conclusions: The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.

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3D graph-based automated segmentation of corneal layers in anterior-segment optical coherence tomography images of mice

Cited by 8 publications

References 13 publications

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation

Automated image analysis of corneal structures in anterior-segment optical coherence tomography and in-vivo confocal microscopy images

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

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