Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network

Jiang, Yun; Tan, Ning; Peng, Tianbo; Zhang, Hai

doi:10.1109/access.2019.2922365

Cited by 77 publications

(37 citation statements)

References 34 publications

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“…Low-quality and artefact-ridden images can affect the performance of segmentation methods. Therefore, the proposed models usually have the following problems [ 23 ]: (1) the downsampling factor of the model is too large, which leads to the feature information of a large number of small blood vessels that is lost in the retinal image, and the information eventually cannot be recovered; (2) the receptive field of the model is too small, which leads to insufficient understanding of local context information, and it is impossible to accurately distinguish pathological regions and blood vessels in the retinal image, causing the incorrect segmentation; (3) the feature extraction capacity of the network structure is insufficient, it is difficult to restore low-level detailed feature information, and a lot of noise is generated in the segmented blood vessel image; and (4) the inability to obtain the accurate information of blood vessels of different sizes results in the inability to accurately detect blood vessel edges and small blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Efficient BFCN for Automatic Retinal Vessel Segmentation

Jiang

Wang

Gao

et al. 2020

Journal of Ophthalmology

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Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively.

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Section: Introductionmentioning

confidence: 99%

Efficient BFCN for Automatic Retinal Vessel Segmentation

Jiang

Wang

Gao

et al. 2020

Journal of Ophthalmology

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“…Jiang et al [46] proposed deep CNN method (D-Net) for retinal vessel segmentation. In the encoder part, the feature loss is reduced by reducing the downsampling factor for tiny vessel segmentation.…”

Section: ) Neural Network-based Methodsmentioning

confidence: 99%

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Imran

Pei

et al. 2019

IEEE Access

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The blood vessels are the primary anatomical structure that can be visible in retinal images. The segmentation of retinal blood vessels has been accepted worldwide for the diagnosis of both cardiovascular (CVD) and retinal diseases. Thus, it requires an appropriate vessel segmentation method for automatic detection of retinal diseases such as diabetic retinopathy and cataract. The detection of retinal diseases using computer-aided diagnosis (CAD) can help people to avoid the risks of visual impairment and save medical resources. This survey presents a comparative analysis of various machine learning and deep learning-based methods for automated blood vessel segmentation in retinal images. This paper briefly describes fundus photography, publicly available retinal databases, pre-processing and post-processing techniques for retinal vessels segmentation. A comprehensive review of the state of the art supervised and unsupervised blood vessel segmentation methodologies are presented in this paper. The objective of this study is to establish a professional structure to familiarize an individual with up-to-date vessel segmentation techniques. Moreover, we compared these approaches to the dataset, evaluation metrics, pre-processing and post-processing steps, feature extraction, segmentation methods, and induced results.

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“…The work of these two stages should have controlled by the accuracy requirements, where Ep is calculated using equation (4).…”

Section: B Convolutional Network Training Processmentioning

confidence: 99%

Magnetic Resonance Image Segmentation Based on Multi-Scale Convolutional Neural Network

Hao¹,

Li²,

Hou

2020

IEEE Access

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The incidence of nervous system and soft tissue is getting higher, and nuclear magnetic resonance is the preferred method of examination and is widely used. The segmentation of brain magnetic resonance (MR) images is the key to the subsequent operations such as three-dimensional reconstruction, quantitative analysis of normal tissues and diseased tissues. The accuracy of image segmentation is critical in the doctor's assessment with the location, shape, and size of the lesion tissue, as well as the determination of the disease and the correct diagnosis plan. The results of this study indicate that the multi-scale convolutional neural network (MSCNN) model can segment brain tumors accurately and effectively. Through multi-scale input, this paper overcomes the need to select specific input scales according to the size of the tumor, accommodate more neighborhood information from various angles, and adapt to different tumor sizes, thus improving the segmentation accuracy of brain tumors. Based on the same accuracy, the segmentation speed is accelerated to ensure the real-time segmentation further. This method can effectively segment the brain lesion tissue in the nuclear magnetic resonance image, which improves the generalization ability. It can be used for identifying the brain lesion tissue of the nuclear magnetic resonance medical image. INDEX TERMS Magnetic resonance (MR), subsequent operation, image segmentation, multi-scale convolutional neural network (MSCNN).

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Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network

Cited by 77 publications

References 34 publications

Efficient BFCN for Automatic Retinal Vessel Segmentation

Efficient BFCN for Automatic Retinal Vessel Segmentation

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Magnetic Resonance Image Segmentation Based on Multi-Scale Convolutional Neural Network

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