Denoising methods for improving automatic segmentation in OCT images of human eye

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

doi:10.1515/bpasts-2017-0009

Cited by 15 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the incorrect segmentation of the RNFL can lead to inaccurate thickness measurements, leading to under-/over- estimation of the glaucomatous damage 18 . By using the denoising framework as a precursor to automated segmentation/thickness measurement, we could increase the reliability 78 of such clinical tools.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Devalla

Subramanian

Pham

et al. 2019

Sci Rep

View full text Add to dashboard Cite

Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 ‘clean B-scans’ (multi-frame B-scans; signal averaged), and their corresponding ‘noisy B-scans’ (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.

show abstract

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Devalla

Subramanian

Pham

et al. 2019

Sci Rep

View full text Add to dashboard Cite

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

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

“…Our previous research showed that both anisotropic diffusion and waveletbased approaches give good results for improving quality and preserving structural characteristics in OCT images [15]. In the following experiments we used block-matching and 4D filtering (BM4D) algorithm proposed by Maggioni et al [16].…”

Section: B Preprocessingmentioning

confidence: 99%

Volumetric segmentation of human eye blood vessels based on OCT images

Stankiewicz

Marciniak

Dąbrowski

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-In this paper we present a method for volumetric segmentation of retinal vessels based on 3D OCT images of human macula. The proposed hybrid method is comprised of two steps: detailed extraction of superficial blood vessels indicators visible in 2D projection of retina layers followed by an axial inspection of inner retina to determine exact depth position of each vessel. The segmentation procedure is improved by application of block-matching and 4D filtering (BM4D) algorithm for noise reduction. The 3D reconstruction of vascular structure was performed for 10 normal subjects examined with Avanti AngioVue OCT device. The automated segmentation results were validated against the manual segmentation performed by an expert giving the accuracy of 95.2%.

show abstract

Denoising methods for improving automatic segmentation in OCT images of human eye

Cited by 15 publications

References 27 publications

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

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

Volumetric segmentation of human eye blood vessels based on OCT images

Contact Info

Product

Resources

About