Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration

Chen, Zailiang; Li, Dabao; Shen, Hailan; Mo, Hailan; Zeng, Ziyang; Wei, Hao

doi:10.1016/j.optlastec.2019.105830

Cited by 30 publications

(20 citation statements)

References 16 publications

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“…OCT is used for the diagnosis of different ocular diseases, such as age-related macular degeneration (AMD), retinal vein occlusion, and diabetic macular edema [338]. U-net has been used on OCT for segmentation of retinal layers [339]- [341], blood vessels [342], fluid regions [343], [344], and Drusen [345]. Other uncommon applications are segmentation of blood vessels in digital subtraction angiography (DSA) [68], [346], [347], white matter tract segmentation in diffusion tensor imaging (DTI) [30], iris segmentation in iris imaging [37], tumor detection in mammograms [56], and capillary segmentation in nailfold capillaroscopy [348].…”

Section: H Other Modalitiesmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: H Other Modalitiesmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…We compared our performance on the 5 OCT volumes of our test set with the one of Rashno et al [2] and found a very slight improvement in results of 1%. We had more difficulties to compare our results to those of Chen et al [11], who report a mean Dice Score of 94%, because it was not made clear which OCT volumes were considered in the training and testing phases of their work. To avoid these issues, we have detailed our results for each volume to facilitate future comparisons in Tab 7.…”

Section: Discussionmentioning

confidence: 80%

“…Venhuizen et al [10] proposed a cascade of two U-Nets with one extracting the region of interest and the second segmenting the fluid regions. Chen et al [11] [14].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based retinal fluid segmentation in optical coherence tomography images using a cascade of ENets

Loizillon

Meurée²,

Breuil³

et al. 2021

Preprint

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Optical coherence tomography (OCT) is a non-invasive, painless and reproducible examination which allows ophthalmologists to visualize retinal layers. This imaging modality is useful to detect diseases such as diabetic macular edema (DME) or age related macular degeneration (AMD), which are associated with fluid accumulations. In this paper, a cascade of deep convolutional neural networks is proposed using ENets for the segmentation of fluid accumulations in OCT B-Scans. After denoising the B-Scans, a first ENet extracts the region of interest (ROI) between the inner limiting membrane (ILM) and the Bruch's membrane (BM), whereas the second ENet segments the fluid in the ROI. A random forest classifier was applied on the segmented fluid regions to reject false positive. Our framework was trained on three different datasets with several diseases such as diabetic retinopathy (DR) and AMD. Our method achieves an average Dice Score for fluid segmentation of 0.80, 0.83 and 0.83 on the UMN DME, UMN AMD and Kermany datasets respectively.

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“…Ronneberger et al were inspired by the FCN network and proposed the U-Net [10], which combines deep semantic information and spatial information through encoder blocks, decoder blocks and skip connection. U-Net architectures, having achieved the best results in many medical image segmentation tasks, are widely used in OCT segmentation, such as optic nerve head tissues segmentation [11], drusen segmentation [12], intraretinal cystoid fluid (IRC) segmentation [13], fluid regions segmentation [14], and retinal layers segmentation [15]. Lu et al [16], achieving the best results in the RETOUCH competition, applied Graph-Cut to perform layer segmentation on OCT images as pre-processing and utilize U-Net to segment 3 types of fluid.…”

Section: Introductionmentioning

confidence: 99%

MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images

Wen

Sun

2020

Algorithms

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Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.

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Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration

Cited by 30 publications

References 16 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Deep-learning based retinal fluid segmentation in optical coherence tomography images using a cascade of ENets

MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images

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