A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Lu, Jianwei; Xu, Yixuan; Chen, Mingle; Luo, Ye

doi:10.3390/sym10110607

Cited by 31 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 6 compares the performance of different methods for retinal vessel segmentation on the CHASE dataset. Compared with other studies [ 27 , 41 – 45 ], the method in this paper reaches the highest value of sensitivity, specificity, accuracy, and F -measure. Through the analysis of Tables 4 – 6 , some indicators of the method in this paper have been improved compared with that of the listed references.…”

Section: Methodsmentioning

confidence: 58%

Efficient BFCN for Automatic Retinal Vessel Segmentation

Jiang

Wang

Gao

et al. 2020

Journal of Ophthalmology

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 58%

Efficient BFCN for Automatic Retinal Vessel Segmentation

Jiang

Wang

Gao

et al. 2020

Journal of Ophthalmology

View full text Add to dashboard Cite

show abstract

“…In the network structure, there are only convolution layer and pooling layer. There is no full connection layer [12,13]. The difference of the two network structures is that up-sampling and down-sampling in U-Net network adopt the same level of convolution operation.…”

Section: U-net Networkmentioning

confidence: 99%

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

et al. 2019

View full text Add to dashboard Cite

Aviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. When considering the spatial limitation of aviation bearing assembled image acquisition, the dynamic distribution of balls and the interference of lubricating grease on the surface, a dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is designed to achieve the accurate segmentation of the local ball region of aviation bearing. Subsequently, an incomplete circle fitting algorithm is designed based on the segmented local ball image and Hough transform principle. These two algorithms make the measurement error of aviation bearing ball size less than 100 μm. Using bearings validates the algorithm. The results show that the accuracy of dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is over 99%. At the same time, on the basis of accurate segmentation in aviation bearing local ball, the designed Hough circle algorithm is used for circle detection. The experimental results show that the false detection rate of mis-assembly and miss-assembly of balls is less than 3%. Further, the goal of zero-missed detection of mis-assembly and miss-assembly of balls in aviation bearing is achieved. The accurate segmentation of aviation bearing local ball and the effective identification of mis-assembly and miss-assembly of balls are realized. This method can provide a theory for the improvement of mis-assembly and miss-assembly of balls detection in aviation bearing. Furthermore, it has high application value.

show abstract

“…The deep learning theory has greatly improved on the conventional machine learning methods [47,48]. Recently, these deep learning methods have been successfully applied in the automated segmentation of blood vessels [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. A more detailed description can be found in the literature [68].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

2020

View full text Add to dashboard Cite

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

show abstract

A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Cited by 31 publications

References 30 publications

Efficient BFCN for Automatic Retinal Vessel Segmentation

Efficient BFCN for Automatic Retinal Vessel Segmentation

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Contact Info

Product

Resources

About