Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images

Stankiewicz, Agnieszka; Marciniak, Tomasz; Dąbrowski, Adam; Stopa, Marcin; Rakowicz, Piotr; Marciniak, Elzbieta

doi:10.1515/mms-2016-0016

Cited by 17 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We tackle here the task of segmenting 2D OCT images, while some consideration has been recently given to improving the segmentation of 3D retina layers. One such example is the work of Stankiewicz, which applies graph theory to deal with low quality OCT images [ 14 ].…”

Section: Discussion and Related Workmentioning

confidence: 99%

Interleaving Automatic Segmentation and Expert Opinion for Retinal Conditions

et al. 2021

View full text Add to dashboard Cite

Optical coherence tomography (OCT) has become the leading diagnostic tool in modern ophthalmology. We are interested here in developing a support tool for the segmentation of retina layers. The proposed method relies on graph theory and geodesic distance. As each retina layer is characterised by different features, the proposed method interleaves various gradients during detection, such as horizontal and vertical gradients or open-closed gradients. The method was tested on a dataset of 750 OCT B-Scan Spectralis provided by the Ophthalmology Department of the County Emergency Hospital Cluj-Napoca. The method has smaller signed error on layers B1, B7 and B8, with the highest value of 0.43 pixels. The average value of signed error on all layers is −1.99 ± 1.14 px. The average value for mean absolute error is 2.60 ± 0.95 px. Since the target is a support tool for the human agent, the ophthalmologist can intervene after each automatic step. Human intervention includes validation or fine tuning of the automatic segmentation. In line with design criteria advocated by explainable artificial intelligence (XAI) and human-centered AI, this approach gives more control and transparency as well as more of a global perspective on the segmentation process.

show abstract

Section: Discussion and Related Workmentioning

confidence: 99%

Interleaving Automatic Segmentation and Expert Opinion for Retinal Conditions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…First, small drusen deposits require highly-sensitive detection techniques and second, segmentation errors need to be avoided at the inner boundary of RPE. Disadvantages of algorithms that analyze elevation maps from segmentation results can be summarized as: (1) The segmentation based on graph search often fails on drusen with abrupt elevation, because the gradients between layers are not smoothly connected and searching for a neighboring pixel with the lowest connection cost is not very reliable [41,62]. In addition, it is common for graph search segmentation algorithms to intentionally neglect small reflectance changes in the definition of cost functions, considering the effect of speckle noise.…”

Section: Discussionmentioning

confidence: 99%

Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography

Zhao¹,

Camino²,

Wang³

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

We introduce a method to automatically detect drusen in dry age-related macular degeneration (AMD) from optical coherence tomography with minimum need for layer segmentation. The method is based on the detection of drusen areas in C-scans at certain distances above the Bruch's membrane, circumventing the difficult task of pathologic retinal pigment epithelium segmentation. All types of drusen can be detected, including the challenging subretinal drusenoid deposits (pseudodrusen). The high sensitivity and accuracy demonstrated here shows its potential for detection of drusen onset in early AMD.

show abstract

“…Since 2012 the graph-search methods proved one of the most accurate retina layers segmentation for healthy and pathological cases. Their disadvantage, however, is the need for extensive image preprocessing (primarily noise suppression) [27,28] and careful selection of parameters for each dataset to make the designed approach suitable for the task. Additionally, the complexity and high time consumption make them inadequate for real-time application in a clinical setting.…”

Section: Retinal Layers Segmentationmentioning

confidence: 99%

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

Stankiewicz

Marciniak

Dąbrowski

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

show abstract

Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images

Cited by 17 publications

References 16 publications

Interleaving Automatic Segmentation and Expert Opinion for Retinal Conditions

Interleaving Automatic Segmentation and Expert Opinion for Retinal Conditions

Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

Contact Info

Product

Resources

About