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

Stankiewicz, Agnieszka; Marciniak, Tomasz; Dąbrowski, Adam; Stopa, Marcin; Marciniak, Elzbieta; Obara, Bogusław

doi:10.3390/s21227521

Cited by 11 publications

(6 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the studies included in this review solely reported either PSNR or SNR of the denoised images in comparison to multiple reference tests, it remains uncertain whether DL provides significant assistance in image denoising and speckle reduction. Ideally, the impact of DL for denoising OCT images in ophthalmology should be demonstrated in practice-based settings and validated by its ability to improve further objectives such as detection [60], classification [61], and segmentation [62], which the majority of included studies did not consider.…”

Section: Discussionmentioning

confidence: 99%

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review

Ahmed,

Zhang,

Donnan

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—PSNR, contrast-to-noise ratio—CNR, and structural similarity index metric—SSIM). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (n = 37) and the Optic Nerve Head (ONH) (n = 4). A wide range of image quality metrics was used; PSNR and SNR that ranged between 8 and 156 dB. The minority of studies (n = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a PSNR or SNR value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.

show abstract

Section: Discussionmentioning

confidence: 99%

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review

Ahmed,

Zhang,

Donnan

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Deep neural networks are also suitable for effective segmentation of the preretinal space [ 3 ]. In this case, effective segmentation of the posterior cortical vitreous (PVC) and inner limiting membrane (ILM) is required.…”

Section: Overview Of Contributionmentioning

confidence: 99%

Biometric Technologies Based on Optical Coherence Tomography

Marciniak

2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the image segmentation task, the Dice coefficient will be more sensitive to the segmentation internal data, while the Hausdorff distance is sensitive to the segmentation boundary data. The Hausdorff distance calculation formula is derived from Formula (10), Formula (11), Formula (12).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

“…Recently, deep learning has achieved remarkable results in visual image recognition tasks. 12 The application of image segmentation in medicine can mark the lesions and extract features for diagnosis and treatment. The deep convolutional network has better applications in medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Liu

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

Currently, deep learning has become more and more mature in the field of medical image segmentation. Through using the computer, the deep learning models established can completely help doctors to perform medical image segmentation. Most of the current deep learning models are based on Unet. The U-shaped structure and the skip connection layer of Unet can effectively achieve precise image segmentation. However, for complicated images, the network structure of Unet is not sufficient enough. In response to this problem, some scholars have designed Unet++ by adding a denser skip connection layer to U-Net. Compared to U-Net, Unet++ is more effective in dealing with complex images, but it has drawbacks in many aspects, and there is still a large loss of eigenvalues in the skip connection and upsampling processes. To address these issues, this paper uses the channel and attention mechanism to improve the Unet++ model to obtain better image segmentation efficiency and accuracy. Meanwhile, based on Unet++, this paper designs a new model called CA-Unet++.The proposed model uses the channel module and the attention module to solve the eigenvalues loses in the long-distance skip connection process and the upsampling process, respectively. The experimental results and data analysis shows that our proposed CA-Unet++

show abstract

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

Cited by 11 publications

References 69 publications

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review

Biometric Technologies Based on Optical Coherence Tomography

CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Contact Info

Product

Resources

About