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DT-Net:Joint Dual-Input Transformer and CNN for retinal vessel segmentation

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Computers, Materials & Continua 2023年第9期76卷 3393-3411页

作者： Wenran Jia Simin Ma Peng Geng Yan Sun School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuangChina School of Mathematics and Information Science Zhangjiakou UniversityZhangjiakouChina

retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many *** acquired fundus images generally have the following problems:uneven illumination,high noise,and complex *** makes vessel segmentation very *** methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the *** address the limitations of convolution by introducing the transformer into retinal vessel ***,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named ***,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder ***,the local prediction results are obtained by the side output *** accuracy of the vessel segmentation is improved by the hybrid loss *** results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1.

关键词： retinal vessel segmentation deformable convolution multi-scale transformer hybrid loss function

Modified Anam-Net Based Lightweight Deep Learning Model for retinal vessel segmentation

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Computers, Materials & Continua 2022年第10期73卷 1501-1526页

作者： Syed Irtaza Haider Khursheed Aurangzeb Musaed Alhussein College of Computer and Information Sciences King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The accurate segmentation of retinal vessels is a challenging taskdue to the presence of various pathologies as well as the low-contrast ofthin vessels and non-uniform illumination. In recent years, encoder-decodernetworks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we proposea lightweight convolutional neural network (CNN)-based encoder-decoderdeep learning model for accurate retinal vessels segmentation. The proposeddeep learning model consists of encoder-decoder architecture along withbottleneck layers that consist of depth-wise squeezing, followed by fullconvolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, whichwas tested on CT images for COVID-19 identification. For our lightweightmodel, we used a stack of two 3 × 3 convolution layers (without spatialpooling in between) instead of a single 3 × 3 convolution layer as proposedin Anam-Net to increase the receptive field and to reduce the trainableparameters. The proposed method includes fewer filters in all convolutionallayers than the original Anam-Net and does not have an increasing numberof filters for decreasing resolution. These modifications do not compromiseon the segmentation accuracy, but they do make the architecture significantlylighter in terms of the number of trainable parameters and computation *** proposed architecture has comparatively fewer parameters (1.01M) thanAnam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the otherrecent works. The proposed model does not require any problem-specificpre- or post-processing, nor does it rely on handcrafted features. In addition,the attribute of being efficient in terms of segmentation accuracy as well aslightweight makes the proposed method a suitable candidate to be used in thescreening platforms at

关键词： Anam-Net convolutional neural network cross-database training data augmentation deep learning fundus images retinal vessel segmentation semantic segmentation

SepFE:Separable Fusion Enhanced Network for retinal vessel segmentation

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2465-2485页

作者： Yun Wu Ge Jiao Jiahao Liu College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable ***,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular *** view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional *** complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for *** ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the ***,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct ***,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is *** superiority of SepFE is further demonstrated through comparisons with other advanced methods.

关键词： retinal vessel segmentation U-Net depth-wise separable convolution feature fusion

An Ensemble retinal vessel segmentation Based on Supervised Learning in Fundus Images

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Chinese Journal of Electronics 2016年第3期25卷 503-511页

作者： ZHU Chengzhang ZOU Beiji XIANG Yao CUI Jinkai WU Hui School of Information Science and Engineering Central South University Mobile Health Ministry of Education-China Mobile Joint Laboratory

An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree(CART). Finally, an Ada Boost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction(DRIVE)database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new retinal images for screening(RIS) database. The average accuracy,sensitivity, and specificity of improved method are 0.9535,0.8319 and 0.9607, respectively. It has potential applications for computer-aided diagnosis and disease screening.

关键词： Fundus images retinal vessel segmentation Feature extraction Divergence of vector field Computer-aided diagnosis

MIA-UNet:Multi-Scale Iterative Aggregation U-Network for retinal vessel segmentation

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Computer Modeling in Engineering & Sciences 2021年第11期129卷 805-828页

作者： Linfang Yu Zhen Qin Yi Ding Zhiguang Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care *** fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial *** the same time,important security and privacy issues have *** data uploaded to the cloud platform involves more personal attributes,health status and medical application data of *** leaked,abused or improperly disclosed,personal information security will be ***,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health *** meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile *** this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal ***,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and ***,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and *** with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%

关键词： retinal vessel segmentation security and privacy redesigned skip connection feature maps aggregation hybrid loss function

Comparative Analysis of Pre-process Pipelines For Automatic retinal vessel segmentation

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Comparative Analysis of Pre-process Pipelines For Automatic ...

Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image segmentation

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作者： Gaoyi Lei Yuanqing Xia Wei Zhang Duanduan Chen Defeng Wang School of automation Beijing Institute of Technology School of Information Science and Engineering Zaozhuang University School of Life Sciences Beijing Institute of Technology Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University

retinal vessel structure is an unique individual characteristic and important biology marker of many diseases, like Diabetic Retinopathy（DR）, cardiovascular ailment, and so on. Automatic retinal vessel segmentation can be used to assist the early diagnosis of above diseases, but suffers from the poor quality and low contrast of fundus images. To eliminate the noise in the fundus images, many pre-process pipelines are designed to normalize and enhance the fundus images. However, the specific operations in pre-process pipelines of the fundus images haven’t been distinguished from operations in normalization of natural images. This paper collects a dozen of pre-process pipelines from published retinal vessel segmentation researches, and proposes five general patterns of these pre-process pipelines. Furthermore, we test the flexibility of five classical pre-process pipelines on public retinal vessel datasets with a Dense-UNet model. Experiments demonstrate that the "Hiera" pre-process pipeline and the"DUNet" pre-process pipeline outperform the rest pipelines in assisting the Dense-UNet to segment the retinal vessels.

关键词： retinal vessel segmentation deep learning pre-process pipeline

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 705-716页

作者： Muwei Jian Ronghua Wu Hongyu Chen Lanqi Fu Chengdong Yang School of Information Science and Technology Linyi UniversityLinyi276000China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China School of Science&Engineering University of LimerickLimerickV94 T9PXIreland

In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation *** address this challenge,we design a Dual-Branch-UNet framework,which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image *** be more explicit,we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original ***,image features are combined at each layer to produce richer semantic data and the model’s capacity is adjusted to various input ***,in the lower sampling section,we give up pooling and conduct the lower sampling by convolution operation to control step size for information *** also employ an attentionmodule in the decoder stage to filter the image noises so as to lessen the response of irrelevant *** are verified and compared on the DRIVE and ARIA datasets for retinal vessels *** proposed Dual-Branch-UNet has proved to be superior to other five typical state-of-the-art methods.

关键词： Convolutional neural network medical image processing retinal vessel segmentation