Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
1 Introduction Retinal vessel analysis plays a crucial role in the detection and management of various systemic and ocular diseases,such as diabetic retinopathy,hypertension and cardiovascular disorders[1].Precise seg...1 Introduction Retinal vessel analysis plays a crucial role in the detection and management of various systemic and ocular diseases,such as diabetic retinopathy,hypertension and cardiovascular disorders[1].Precise segmentation of retinal vessels from fundus images enables clinicians to analyze vessel morphology,which can reveal disease progression or underlying conditions.Over recent years,deep learning methods have significantly advanced retinal vessel segmentation.展开更多
Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images ...Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images plays a pivotal role in the early diagnosis,progression monitoring,and treatment planning of DR and related ocular conditions.Traditional convolutional neural networks often struggle with capturing the intricate structures of thin vessels under varied illumination and contrast conditions.Methods:In this study,we propose an improved U-Net-based framework named MSAC U-Net,which enhances feature extraction and reconstruction through multiscale and attention-based modules.Specifically,the encoder replaces standard convolutions with a Multiscale Asymmetric Convolution(MSAC)block,incorporating parallel 1×n,n×1,and n×n kernels at different scales(3×3,5×5,7×7)to effectively capture fine-grained vascular structures.To further refine spatial representation,skip connections are utilized,and the decoder is augmented with dual activation strategies,Squeeze-and-Excitation blocks,and Convolutional Block Attention Modules for improved contextual understanding.Results:The model was evaluated on the publicly available DRIVE dataset.It achieved an accuracy of 96.48%,sensitivity of 88.31%,specificity of 97.90%,and an AUC of 98.59%,demonstrating superior performance compared to several state-of-the-art segmentation methods.Conclusion:The proposed MSAC U-Net provides a robust and accurate approach for retinal vessel segmentation,offering substantial clinical value in the early detection and management of diabetic retinopathy.Its design contributes to enhanced segmentation reliability and may serve as a foundation for broader applications in medical image analysis.展开更多
The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segm...The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently,we propose a novel network(DSeU-net)based on deformable convolution and squeeze excitation residual module.The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel.And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently.We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE,CHASEDB1,and STARE,and the experimental results demonstrate the satisfactory segmentation performance of the network.展开更多
Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researcher...Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks(CNNs), many tasks can be solved by CNNs.In this paper, we propose an encoding-decoding network with a pyramid self-attention module(PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks(FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE;B1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve(AUC) are 82.21%/80.57%,95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE;B1, respectively.展开更多
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 system.The fundus ...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 system.The 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 intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,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 systems.To 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 terminals.In 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 information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared 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%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.展开更多
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...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 effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,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 fusion.Finally,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 obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods.展开更多
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-decodernet...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 time.The 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 the point of care. We evaluated our proposed modelon open-access datasets namely, DRIVE, STARE, and CHASE_DB. Theexperimental results show that the proposed model outperforms several stateof-the-art methods, such as U-Net and its variants, fully convolutional network (FCN), SegNet, CCNet, ResWNet, residual connection-based encoderdecoder network (RCED-Net), and scale-space approx. network (SSANet) in terms of {dice coefficient, sensitivity (SN), accuracy (ACC), and the areaunder the ROC curve (AUC)} with the scores of {0.8184, 0.8561, 0.9669, and0.9868} on the DRIVE dataset, the scores of {0.8233, 0.8581, 0.9726, and0.9901} on the STARE dataset, and the scores of {0.8138, 0.8604, 0.9752,and 0.9906} on the CHASE_DB dataset. Additionally, we perform crosstraining experiments on the DRIVE and STARE datasets. The result of thisexperiment indicates the generalization ability and robustness of the proposedmodel.展开更多
Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high ...Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous 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 vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,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 block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental 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.展开更多
Biomedical image processing is finding useful in healthcare sector for the investigation,enhancement,and display of images gathered by distinct imaging technologies.Diabetic retinopathy(DR)is an illness caused by diab...Biomedical image processing is finding useful in healthcare sector for the investigation,enhancement,and display of images gathered by distinct imaging technologies.Diabetic retinopathy(DR)is an illness caused by diabetes complications and leads to irreversible injury to the retina blood vessels.Retinal vessel segmentation techniques are a basic element of automated retinal disease screening system.In this view,this study presents a novel blood vessel segmentation with deep learning based classification(BVS-DLC)model forDRdiagnosis using retinal fundus images.The proposed BVS-DLC model involves different stages of operations such as preprocessing,segmentation,feature extraction,and classification.Primarily,the proposed model uses the median filtering(MF)technique to remove the noise that exists in the image.In addition,a multilevel thresholding based blood vessel segmentation process using seagull optimization(SGO)with Kapur’s entropy is performed.Moreover,the shark optimization algorithm(SOA)with Capsule Networks(CapsNet)model with softmax layer is employed for DR detection and classification.Awide range of simulations was performed on the MESSIDOR dataset and the results are investigated interms of different measures.The simulation results ensured the better performance of the proposed model compared to other existing techniques interms of sensitivity,specificity,receiver operating characteristic(ROC)curve,accuracy,and F-score.展开更多
Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system fo...Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.展开更多
Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the...Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.展开更多
Objective For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation,a novel multi-level method based on the multi-scale fusion residual neural network(MF2ResU-Net)model is pro...Objective For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation,a novel multi-level method based on the multi-scale fusion residual neural network(MF2ResU-Net)model is proposed.Methods To obtain refined features of retinal blood vessels,three cascade connected UNet networks are employed.To deal with the problem of difference between the parts of encoder and decoder,in MF2ResU-Net,shortcut connections are used to combine the encoder and decoder layers in the blocks.To refine the feature of segmentation,atrous spatial pyramid pooling(ASPP)is embedded to achieve multi-scale features for the final segmentation networks.Results The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity(Sen),specificity(Spe),accuracy(ACC),and area under curve(AUC),the values of which are 0.8013 and 0.8102,0.9842 and 0.9809,0.9700 and 0.9776,and 0.9797 and 0.9837,respectively for DRIVE and CHASE DB1.The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.Conclusion Based on residual connections and multi-feature fusion,the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features,which can provide another diagnosis method for computer-aided Chinese medical diagnosis.展开更多
The segmentation of retinal vessels and coronary angiographs is essential for diagnosing conditions such as glaucoma,diabetes,hypertension,and coronary artery disease.However,retinal vessels and coronary angiographs a...The segmentation of retinal vessels and coronary angiographs is essential for diagnosing conditions such as glaucoma,diabetes,hypertension,and coronary artery disease.However,retinal vessels and coronary angiographs are characterized by low contrast and complex structures,posing challenges for vessel segmentation.Moreover,CNN-based approaches are limited in capturing long-range pixel relationships due to their focus on local feature extraction,while ViT-based approaches struggle to capture fine local details,impacting tasks like vessel segmentation that require precise boundary detection.To address these issues,in this paper,we propose a Global–Local Hybrid Modulation Network(GLHM-Net),a dual-encoder architecture that combines the strengths of CNNs and ViTs for vessel segmentation.First,the Hybrid Non-Local Transformer Block(HNLTB)is proposed to efficiently consolidate long-range spatial dependencies into a compact feature representation,providing a global perspective while significantly reducing computational overhead.Second,the Collaborative Attention Fusion Block(CAFB)is proposed to more effectively integrate local and global vessel features at the same hierarchical level during the encoding phase.Finally,the proposed Feature Cross-Modulation Block(FCMB)better complements the local and global features in the decoding stage,effectively enhancing feature learning and minimizing information loss.The experiments conducted on the DRIVE,CHASEDB1,DCA1,and XCAD datasets,achieving AUC values of 0.9811,0.9864,0.9915,and 0.9919,F1 scores of 0.8288,0.8202,0.8040,and 0.8150,and IOU values of 0.7076,0.6952,0.6723,and 0.6878,respectively,demonstrate the strong performance of our proposed network for vessel segmentation.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate l...As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.展开更多
Cerebrovascular diseases are a widespread threat to human health.The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases.However,the ...Cerebrovascular diseases are a widespread threat to human health.The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases.However,the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation.Therefore,we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images.This method employs a Multiscale Squeeze Attention(MSA)module for channel-wise attention learning,extracting multiscale attention feature maps from angiographic images.To maintain the topological connectivity of vessel segmentation,we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation.We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset.The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation,enabling accurate and complete extraction of vascular structures.The proposed method was extended to coronary angiography images.The results show that the proposed method can accurately extract coronary structures,proving its broad applicability to other vascular segmentation tasks.展开更多
Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical ...Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics.However,achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology.In addition,supervised learning methods heavily rely on high-quality labels for accurate segmentation results,which often necessitate extensive labeling efforts.Here,we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing.Furthermore,we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets,even when confronted with unfamiliar data.Importantly,the dice similarity coefficient exceeded 85%in a rat experiment.Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations.This would greatly benefit future research in life and medicine.展开更多
According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorit...According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.展开更多
Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel se...Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.展开更多
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金supported by the Shuangchuang Ph.D award,Jiangsu,China(No.JSSCBS20210804)the National Natural Science Foundation of China(Grant No.62201460)the Basic Research Programs of Taicang(No.TC2023JC22).
文摘1 Introduction Retinal vessel analysis plays a crucial role in the detection and management of various systemic and ocular diseases,such as diabetic retinopathy,hypertension and cardiovascular disorders[1].Precise segmentation of retinal vessels from fundus images enables clinicians to analyze vessel morphology,which can reveal disease progression or underlying conditions.Over recent years,deep learning methods have significantly advanced retinal vessel segmentation.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2024A1515010987)the Medical Scientific Research Foundation of Guangdong Province(B2024035).
文摘Background:Diabetic retinopathy(DR)is one of the primary causes of visual impairment globally,resulting from microvascular abnormalities in the retina.Accurate segmentation of retinal blood vessels from fundus images plays a pivotal role in the early diagnosis,progression monitoring,and treatment planning of DR and related ocular conditions.Traditional convolutional neural networks often struggle with capturing the intricate structures of thin vessels under varied illumination and contrast conditions.Methods:In this study,we propose an improved U-Net-based framework named MSAC U-Net,which enhances feature extraction and reconstruction through multiscale and attention-based modules.Specifically,the encoder replaces standard convolutions with a Multiscale Asymmetric Convolution(MSAC)block,incorporating parallel 1×n,n×1,and n×n kernels at different scales(3×3,5×5,7×7)to effectively capture fine-grained vascular structures.To further refine spatial representation,skip connections are utilized,and the decoder is augmented with dual activation strategies,Squeeze-and-Excitation blocks,and Convolutional Block Attention Modules for improved contextual understanding.Results:The model was evaluated on the publicly available DRIVE dataset.It achieved an accuracy of 96.48%,sensitivity of 88.31%,specificity of 97.90%,and an AUC of 98.59%,demonstrating superior performance compared to several state-of-the-art segmentation methods.Conclusion:The proposed MSAC U-Net provides a robust and accurate approach for retinal vessel segmentation,offering substantial clinical value in the early detection and management of diabetic retinopathy.Its design contributes to enhanced segmentation reliability and may serve as a foundation for broader applications in medical image analysis.
基金Beijing Natural Science Foundation(No.IS23112)Beijing Institute of Technology Research Fund Program for Young Scholars(No.6120220236)。
文摘The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently,we propose a novel network(DSeU-net)based on deformable convolution and squeeze excitation residual module.The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel.And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently.We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE,CHASEDB1,and STARE,and the experimental results demonstrate the satisfactory segmentation performance of the network.
文摘Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks(CNNs), many tasks can be solved by CNNs.In this paper, we propose an encoding-decoding network with a pyramid self-attention module(PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks(FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE;B1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve(AUC) are 82.21%/80.57%,95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE;B1, respectively.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62072074,62076054,62027827,61902054)the Frontier Science and Technology Innovation Projects of National Key R&D Program(No.2019QY1405)+2 种基金the Sichuan Science and Technology Innovation Platform and Talent Plan(No.2020JDJQ0020)the Sichuan Science and Technology Support Plan(No.2020YFSY0010)the Natural Science Foundation of Guangdong Province(No.2018A030313354).
文摘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 system.The 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 intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,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 systems.To 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 terminals.In 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 information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared 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%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.
基金supported by the Hunan Provincial Natural Science Foundation of China(2021JJ50074)the Scientific Research Fund of Hunan Provincial Education Department(19B082)+6 种基金the Science and Technology Development Center of the Ministry of Education-New Generation Information Technology Innovation Project(2018A02020)the Science Foundation of Hengyang Normal University(19QD12)the Science and Technology Plan Project of Hunan Province(2016TP1020)the Subject Group Construction Project of Hengyang Normal University(18XKQ02)theApplication Oriented SpecialDisciplines,Double First ClassUniversity Project of Hunan Province(Xiangjiaotong[2018]469)the Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development(2018CT5001)the First Class Undergraduate Major in Hunan Province Internet of Things Major(Xiangjiaotong[2020]248,No.288).
文摘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 effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,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 fusion.Finally,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 obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods.
基金The authors extend their appreciation to the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(DRI−KSU−415).
文摘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 time.The 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 the point of care. We evaluated our proposed modelon open-access datasets namely, DRIVE, STARE, and CHASE_DB. Theexperimental results show that the proposed model outperforms several stateof-the-art methods, such as U-Net and its variants, fully convolutional network (FCN), SegNet, CCNet, ResWNet, residual connection-based encoderdecoder network (RCED-Net), and scale-space approx. network (SSANet) in terms of {dice coefficient, sensitivity (SN), accuracy (ACC), and the areaunder the ROC curve (AUC)} with the scores of {0.8184, 0.8561, 0.9669, and0.9868} on the DRIVE dataset, the scores of {0.8233, 0.8581, 0.9726, and0.9901} on the STARE dataset, and the scores of {0.8138, 0.8604, 0.9752,and 0.9906} on the CHASE_DB dataset. Additionally, we perform crosstraining experiments on the DRIVE and STARE datasets. The result of thisexperiment indicates the generalization ability and robustness of the proposedmodel.
基金supported in part by the National Natural Science Foundation of China under Grant 61972267the National Natural Science Foundation of Hebei Province under Grant F2018210148the University Science Research Project of Hebei Province under Grant ZD2021334.
文摘Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous 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 vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,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 block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental 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.
基金Ministry of Education in Saudi Arabia for funding this research work through the project number (IFP-2020-66).
文摘Biomedical image processing is finding useful in healthcare sector for the investigation,enhancement,and display of images gathered by distinct imaging technologies.Diabetic retinopathy(DR)is an illness caused by diabetes complications and leads to irreversible injury to the retina blood vessels.Retinal vessel segmentation techniques are a basic element of automated retinal disease screening system.In this view,this study presents a novel blood vessel segmentation with deep learning based classification(BVS-DLC)model forDRdiagnosis using retinal fundus images.The proposed BVS-DLC model involves different stages of operations such as preprocessing,segmentation,feature extraction,and classification.Primarily,the proposed model uses the median filtering(MF)technique to remove the noise that exists in the image.In addition,a multilevel thresholding based blood vessel segmentation process using seagull optimization(SGO)with Kapur’s entropy is performed.Moreover,the shark optimization algorithm(SOA)with Capsule Networks(CapsNet)model with softmax layer is employed for DR detection and classification.Awide range of simulations was performed on the MESSIDOR dataset and the results are investigated interms of different measures.The simulation results ensured the better performance of the proposed model compared to other existing techniques interms of sensitivity,specificity,receiver operating characteristic(ROC)curve,accuracy,and F-score.
文摘Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572154）, and the National Basic Research Program of China （Grant No.2003CB716104）Acknowledgment I would like to thank YANG Xin, my tutor, SHANG Yan- feng, SUN Kun of Shanghai Children＇s Medical Center, and all the people in 3D Visualization Laboratory of Shanghai Jiaotong University for their help during my research.
文摘Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.
基金Key R&D Projects in Hebei Province(22370301D)Scientific Research Foundation of Hebei University for Distinguished Young Scholars(521100221081)Scientific Research Foundation of Colleges and Universities in Hebei Province(QN2022107)。
文摘Objective For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation,a novel multi-level method based on the multi-scale fusion residual neural network(MF2ResU-Net)model is proposed.Methods To obtain refined features of retinal blood vessels,three cascade connected UNet networks are employed.To deal with the problem of difference between the parts of encoder and decoder,in MF2ResU-Net,shortcut connections are used to combine the encoder and decoder layers in the blocks.To refine the feature of segmentation,atrous spatial pyramid pooling(ASPP)is embedded to achieve multi-scale features for the final segmentation networks.Results The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity(Sen),specificity(Spe),accuracy(ACC),and area under curve(AUC),the values of which are 0.8013 and 0.8102,0.9842 and 0.9809,0.9700 and 0.9776,and 0.9797 and 0.9837,respectively for DRIVE and CHASE DB1.The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.Conclusion Based on residual connections and multi-feature fusion,the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features,which can provide another diagnosis method for computer-aided Chinese medical diagnosis.
基金supported by Natural Science Research Project of Tianjin Education Commission(Grant 2020KJ124)National Natural Science Foundation of China(Grant 11601372)National Key Research and Development Program of China(Grant 2022YFF0706003).
文摘The segmentation of retinal vessels and coronary angiographs is essential for diagnosing conditions such as glaucoma,diabetes,hypertension,and coronary artery disease.However,retinal vessels and coronary angiographs are characterized by low contrast and complex structures,posing challenges for vessel segmentation.Moreover,CNN-based approaches are limited in capturing long-range pixel relationships due to their focus on local feature extraction,while ViT-based approaches struggle to capture fine local details,impacting tasks like vessel segmentation that require precise boundary detection.To address these issues,in this paper,we propose a Global–Local Hybrid Modulation Network(GLHM-Net),a dual-encoder architecture that combines the strengths of CNNs and ViTs for vessel segmentation.First,the Hybrid Non-Local Transformer Block(HNLTB)is proposed to efficiently consolidate long-range spatial dependencies into a compact feature representation,providing a global perspective while significantly reducing computational overhead.Second,the Collaborative Attention Fusion Block(CAFB)is proposed to more effectively integrate local and global vessel features at the same hierarchical level during the encoding phase.Finally,the proposed Feature Cross-Modulation Block(FCMB)better complements the local and global features in the decoding stage,effectively enhancing feature learning and minimizing information loss.The experiments conducted on the DRIVE,CHASEDB1,DCA1,and XCAD datasets,achieving AUC values of 0.9811,0.9864,0.9915,and 0.9919,F1 scores of 0.8288,0.8202,0.8040,and 0.8150,and IOU values of 0.7076,0.6952,0.6723,and 0.6878,respectively,demonstrate the strong performance of our proposed network for vessel segmentation.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金supported by the National Natural Science Foundation of China under Grant Nos.62301330 and 62101346the Guangdong Basic and Applied Basic Research Foundation under Grant Nos.20231121103807001,2022A1515110101the Guangdong Provincial Key Laboratory under Grant No.2023B1212060076.
文摘As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.
基金supported by the National Natural Science Foundation of China(81827805,82130060,61821002,92148205)the National Key Research and Development Program(2018YFA0704100,2018YFA0704104)+5 种基金Project funded by China Postdoctoral Science Foundation(2021M700772)Zhuhai Industry-University-Research Collaboration Program(ZH22017002210011PWC)Jiangsu Provincial Science and Technology Plan Project(BE2023769)Jiangsu Provincial Medical Innovation Center(CXZX202219)Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions,Nanjing Life Health Science and Technology Project(202205045)the Natural Science Foundation of Guangdong Province of China(2021A1515220183,2022B1515020010).
文摘Cerebrovascular diseases are a widespread threat to human health.The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases.However,the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation.Therefore,we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images.This method employs a Multiscale Squeeze Attention(MSA)module for channel-wise attention learning,extracting multiscale attention feature maps from angiographic images.To maintain the topological connectivity of vessel segmentation,we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation.We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset.The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation,enabling accurate and complete extraction of vascular structures.The proposed method was extended to coronary angiography images.The results show that the proposed method can accurately extract coronary structures,proving its broad applicability to other vascular segmentation tasks.
基金supported by grants fromthe State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(2023XAKJ0101031)National Natural Science Foundation of China(81971665)+8 种基金Natural Science Foundation of Fujian Province(2021J011366)Medical and Health Guidance Project of Xiamen(3502Z20214ZD1016)Xiamen Health High-Level Talent Training Program,Ningxia Hui Autonomous Region Key Research and Development Program(2022BEG03127)Fundamental Research Funds for the Central Universities of China(20720210117)Fujian Province Science and Technology Plan Guiding Project(2022Y0002)National Natural Science Foundation of China(62005048)Natural Science Foundation of Fujian Province(2020J01158)Ministry of Education Industry-university Cooperative Education Project(220606053295218)XMU Undergraduate Innovation and Entrepreneurship Training Programs(2023X805,2023X808,2023Y1109).
文摘Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics.However,achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology.In addition,supervised learning methods heavily rely on high-quality labels for accurate segmentation results,which often necessitate extensive labeling efforts.Here,we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing.Furthermore,we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets,even when confronted with unfamiliar data.Importantly,the dice similarity coefficient exceeded 85%in a rat experiment.Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations.This would greatly benefit future research in life and medicine.
基金Project (60872081) supported by the National Natural Science Foundation of ChinaProject (50051) supported by the Program for New Century Excellent Talents in UniversityProject (4092034) supported by the Natural Science Foundation of Beijing
文摘According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.
基金Project supported by the Natural Science Foundation of Fujian Province,China(No.2016J0129)the Educational Commission of Fujian Province of China(No.JAT170180)
文摘Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.
