SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation

Wu, Huisi; Wang, Wei; Zhong, Jiafu; Lei, Baiying; Wen, Zhenkun; Qin, Jing

doi:10.1016/j.media.2021.102025

Cited by 153 publications

(70 citation statements)

References 28 publications

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“…We used out-of-mask avoidance and inside-mask cancellation to reduce the risk of overscoring from blood vessels. (In the future, it might also be possible to remove blood vessels without subtraction by using previous reports on extracting blood vessel regions in fundus images 24 ).…”

Section: Discussionmentioning

confidence: 99%

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration

Motozawa

Miura

Ochiai

et al. 2022

Sci Rep

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The retinal pigment epithelium (RPE) is essential for the survival and function of retinal photoreceptor cells. RPE dysfunction causes various retinal diseases including age-related macular degeneration (AMD). Clinical studies on ES/iPS cell-derived RPE transplantation for RPE dysfunction-triggered diseases are currently underway. Quantification of the diseased RPE area is important to evaluate disease progression or the therapeutic effect of RPE transplantation. However, there are no standard protocols. To address this issue, we developed a 2-step software that enables objective and efficient quantification of RPE-disease area changes by analyzing the early-phase hyperfluorescent area in fluorescein angiography (FA) images. We extracted the Abnormal region. This extraction was based on deep learning-based discrimination. We scored the binarized extracted area using an automated program. Our program’s performance for the same eye from the serial image captures was within 3.1 ± 7.8% error. In progressive AMD, the trend was consistent with human assessment, even when FA images from two different visits were compared. This method was applicable to quantifying RPE-disease area changes over time, evaluating iPSC-RPE transplantation images, and a disease other than AMD. Our program may contribute to the assessment of the clinical course of RPE-disease areas in routine clinics and reduce the workload of researchers.

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Section: Discussionmentioning

confidence: 99%

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration

Motozawa

Miura

Ochiai

et al. 2022

Sci Rep

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“…Considering the large-scale variants of vessels and semantic variants existing in fundus images, Wu, et al [117] proposed to adjust the receptive field adaptively to capture multi-scale features, they also adaptively fused features to extract more semantic information. They obtained a good result but still need to pay more attention to thin vessels.…”

Section: U-net For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network.

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“…The change characteristics of retinal vessels width obtained after segmentation can be used to detect and analyze hypertension [ 2 ]. Therefore, the current research on automatic segmentation of retinal blood vessels is an important development direction in this field, and is also of great significance to the study of related retinal diseases [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation

et al. 2022

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The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retinal vessels images is of great significance for the diagnosis of visually threatening diseases. In recent years, especially the convolutional neural networks (CNN) based on UNet and its variant have been widely used in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-distance semantic information interaction well due to the local computing characteristics of convolution operation, which limits the development of medical image segmentation tasks. Transformer, currently popular in computer vision, has global computing features, but due to the lack of low-level details, local feature information extraction is insufficient. In this paper, we propose Patches Convolution Attention based Transformer UNet (PCAT-UNet), which is a U-shaped network based on Transformer with a Convolution branch. We use skip connection to fuse the deep and shallow features of both sides. By taking advantage of the complementary advantages of both sides, we can effectively capture the global dependence relationship and the details of the underlying feature space, thus improving the current problems of insufficient extraction of retinal micro vessels feature information and low sensitivity caused by easily predicting of pixels as background. In addition, our method enables end-to-end training and rapid inference. Finally, three publicly available retinal vessels datasets (DRIVE, STARE and CHASE_DB1) were used to evaluate PCAT-UNet. The experimental results show that the proposed PCAT-UNET method achieves good retinal vessel segmentation performance on these three datasets, and is superior to other architectures in terms of AUC, Accuracy and Sensitivity performance indicators. AUC reached 0.9872, 0.9953 and 0.9925, Accuracy reached 0.9622, 0.9796 and 0.9812, Sensitivity reached 0.8576, 0.8703 and 0.8493, respectively. In addition, PCAT-UNET also achieved good results in two other F1-Score and Specificity indicators.

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SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation

Cited by 153 publications

References 28 publications

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration

Retinal Vessel Segmentation Using Deep Learning: A Review

PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation

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