Marker controlled watershed transform for intra-retinal cysts segmentation from optical coherence tomography B-scans

Girish, G. N.; Kothari, Abhishek; Rajan, Jeny

doi:10.1016/j.patrec.2017.12.019

Cited by 12 publications

(13 citation statements)

References 21 publications

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“…Comparison mean (standard deviation) of recall, precision, and DC of the proposed method on all (test + train) and train spectralis images of the OPTIMA dataset with the methods proposed in Refs [29]. and[31,32].…”

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confidence: 99%

An unsupervised hierarchical approach for automatic intra‐retinal cyst segmentation in spectral‐domain optical coherence tomography images

2020

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Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions. Thus, automatic segmentation of IRCs can be beneficial to investigate disease progression. Methods: In this study, automatic IRC segmentation is accomplished by building three different masks in three unsupervised segmentation levels of a hierarchical framework. In the first level, the ROI-mask (R-mask) is built, and the retina area is cropped based on this mask. In the second level, the prune-mask (P-mask) is built, and the searching space is significantly reduced toward the target objects using this mask; and finally in the third level, by applying the Markov random field (MRF) model and employing intensity and contextual information, the cyst mask (C-mask) is extracted. Results: The proposed method is evaluated on three datasets including OPTIMA, UMN, and KER-MANY datasets. The experimental results showed that the proposed method is effective with a mean dice coefficient rate of 0.74, 0.75 and 0.79 by the intersection of ground truths on the OPTIMA, UMN and KERMANY datasets, respectively. Conclusion: The proposed method outperforms the state-of-the-art methods on the OPTIMA and UMN datasets while achieving comparable results to the most recently proposed method on the KERMANY dataset.

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confidence: 99%

An unsupervised hierarchical approach for automatic intra‐retinal cyst segmentation in spectral‐domain optical coherence tomography images

2020

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“…For this study, the watershed segmentation method was utilized to overcome this limitation. Although a well‐established technique in image segmentation , the application of the watershed transformation to OCT data is relatively new . This method is, however, sensitive to speckle.…”

Section: Discussionmentioning

confidence: 99%

“…The speckle patterns were suppressed in this study by using the MIPs over the 27 independent wavelengths. Application of the watershed transformation to standard OCT images would require another means of speckle reduction, for example the Bayesian non local means filter as used by Girish et al .…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral optical coherence tomography for in vivo visualization of melanin in the retinal pigment epithelium

Harper

Konegger

Augustin

et al. 2019

Journal of Biophotonics

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Previous studies for melanin visualization in the retinal pigment epithelium (RPE) have exploited either its absorption properties (using photoacoustic tomography or photothermal optical coherence tomography [OCT]) or its depolarization properties (using polarization sensitive OCT). However, these methods are only suitable when the melanin concentration is sufficiently high. In this work, we present the concept of hyperspectral OCT for melanin visualization in the RPE when the concentration is low. Based on white light OCT, a hyperspectral stack of 27 wavelengths (440‐700 nm) was created in post‐processing for each depth‐resolved image. Owing to the size and shape of the melanin granules in the RPE, the variations in backscattering coefficient as a function of wavelength could be identified—a result which is to be expected from Mie theory. This effect was successfully identified both in eumelanin‐containing phantoms and in vivo in the low‐concentration Brown Norway rat RPE.

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“…The watershed algorithm is widely used in the medical image segmentation, such as lesion segmentation of breast in mammogram 29 and ultrasound images, 30,31 intraretinal cysts segmentation in OCT images, 32 lymphoma segmentation in computerized tomography (CT) images, 33 and malignant lesion segmentation in images acquired by magnetic resonance imaging. 34 The watershed algorithm can be an effective segmentation method if it is used with auxiliary processing before and after segmentation.…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic segmentation of abnormal capillary nonperfusion regions in optical coherence tomography angiography images using marker-controlled watershed algorithm

2018

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Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression. Therefore, automatic segmentation and quantification of abnormal CNP regions can be helpful to monitor the patient's treatment process. We propose an automatic method for segmentation of abnormal CNP regions on the superficial and deep capillary plexuses of optical coherence tomography angiography (OCTA) images using the marker-controlled watershed algorithm. The proposed method has three main steps. In the first step, original images are enhanced using the vesselness filter and then foreground and background marker images are computed. In the second step, abnormal CNP region candidates are segmented using the marker-controlled watershed algorithm, and in the third step, the candidates are modeled using an undirected weighted graph and finally, by applying merging and removing procedures correct abnormal CNP regions are identified. The proposed method was evaluated on a dataset with 36 normal and diabetic subjects using the ground truth obtained by two observers. The results show the proposed method outperformed some of the state-of-the-art methods on the superficial and deep capillary plexuses according to the most important metrics.

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Marker controlled watershed transform for intra-retinal cysts segmentation from optical coherence tomography B-scans

Cited by 12 publications

References 21 publications

An unsupervised hierarchical approach for automatic intra‐retinal cyst segmentation in spectral‐domain optical coherence tomography images

An unsupervised hierarchical approach for automatic intra‐retinal cyst segmentation in spectral‐domain optical coherence tomography images

Hyperspectral optical coherence tomography for in vivo visualization of melanin in the retinal pigment epithelium

Automatic segmentation of abnormal capillary nonperfusion regions in optical coherence tomography angiography images using marker-controlled watershed algorithm

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