Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

Atlı, İbrahim; Gedik, O. Serdar

doi:10.1016/j.jestch.2020.07.008

Cited by 59 publications

(40 citation statements)

References 19 publications

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“…Finally, the feature fusion module is used to aggregate the information of coarse and fine branches. We validated this method on the DRIVE, STARE, and CHASE data sets, and the experimental results showed that our method has better performance in retinal vessel segmentation than some latest algorithms, such as WA-Net [35] and Sine-Net [42].…”

Section: Discussionmentioning

confidence: 98%

Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Jiang

Yao

et al. 2021

Symmetry

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The segmentation of retinal vessels is critical for the diagnosis of some fundus diseases. Retinal vessel segmentation requires abundant spatial information and receptive fields with different sizes while existing methods usually sacrifice spatial resolution to achieve real-time reasoning speed, resulting in inadequate vessel segmentation of low-contrast regions and weak anti-noise interference ability. The asymmetry of capillaries in fundus images also increases the difficulty of segmentation. In this paper, we proposed a two-branch network based on multi-scale attention to alleviate the above problem. First, a coarse network with multi-scale U-Net as the backbone is designed to capture more semantic information and to generate high-resolution features. A multi-scale attention module is used to obtain enough receptive fields. The other branch is a fine network, which uses the residual block of a small convolution kernel to make up for the deficiency of spatial information. Finally, we use the feature fusion module to aggregate the information of the coarse and fine networks. The experiments were performed on the DRIVE, CHASE, and STARE datasets. Respectively, the accuracy reached 96.93%, 97.58%, and 97.70%. The specificity reached 97.72%, 98.52%, and 98.94%. The F-measure reached 83.82%, 81.39%, and 84.36%. Experimental results show that compared with some state-of-art methods such as Sine-Net, SA-Net, our proposed method has better performance on three datasets.

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Section: Discussionmentioning

confidence: 98%

Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Jiang

Yao

et al. 2021

Symmetry

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“…In recent years, automatic or semi-automatic segmentation methods using deep learning algorithms have become popular in the vascular biology [ 16 ] and placental biology fields [ 17 ]. Convolution neural networks (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This proposed sample preparation and imaging pipeline would be applicable to other vascular-rich organs [13][14][15]. In recent years, automatic or semi-automatic segmentation methods using deep learning algorithms have become popular in the vascular biology [16] and placental biology fields [17]. Convolution neural networks (e.g.…”

Section: Discussionmentioning

confidence: 99%

A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

Tun

Poologasundarampillai

Bischof

et al. 2021

J. R. Soc. Interface.

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Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure–function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure–function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.

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“…Different modalities of retinal images and DL techniques have been extensively used in the literature, mostly on the detection of diabetic retinopathy [4]- [6], detection and assessment of macular edema and fluid accumulation [7]- [9], segmentation of retinal layers [10]- [12] and blood vessels [13]- [15], among others. In contrast, there are few works concerned with automatic ERM detection.…”

Section: Introductionmentioning

confidence: 99%

Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

et al. 2021

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Epiretinal membrane (ERM) is an eye disease that affects 7% of the world population, with a higher incidence in people over 75 years old. If left untreated, it can lead to complications in the central vision, resulting in severe vision loss. Early detection is important for progress follow-up, treatment monitoring, and to avoid total vision loss. Optical coherence tomography, a non-invasive retina imaging technique, can be used for effective detection and monitoring of this condition. To date, automatic methods to detect ERM have received little attention in the research literature. This article describes the application of deep learning to the automatic detection of ERM. The proposed solution is based on four widely used convolutional neural network architectures adapted to the task using transfer learning, and fine-tuned with a proprietary dataset. The architectures were specialized by optimizing the network hyperparameters and two loss functions, cross-entropy and focal loss. A detailed description of the methods is provided, complemented with an exhaustive evaluation of their performance. Overall, the methods reached an accuracy of 99.7%, with sensitivity and specificity of 99.47% and 99.93%, respectively. The results showed that transfer learning enabled a successful use of deep learning to detect ERM in optical coherence tomography retinal images, even when only relatively small training datasets are available.INDEX TERMS Artificial intelligence, deep learning, epiretinal membrane, macular puker, neural networks, optical coherence tomography, transfer learning.

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Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation

Cited by 59 publications

References 19 publications

Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

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