Retinal Vessel Segmentation Research Articles

Assessment of retinal blood vessel segmentation using U-Net model: A deep learning approach

Segmentation of retinal blood vessels from fundus images is vital to assist ophthalmologists in diagnosing different eye diseases like Arteriosclerosis, Glaucoma, Diabetic Retinopathy, Hypertension, and Choroidal Neovascularization. Accurate detection and timely treatment of these eye diseases can prevent irreversible impairment of vision. In computer-aided automatic diagnosis, Preprocessing of fundus images is necessary to reduce the uneven retinal image illumination, as well as the undesirable effect arising from the contrast differences between the retinal blood vessels and the background. In this study, the Hessian-based Frangi vesselness filter is proposed to enhance vasculature in fundus images. This approach seeks to extract thin vessels to diminish the intensity disparity between thick and thin vessels. Its accuracy has never, however, been thoroughly evaluated. To verify the effectiveness of the suggested filter for boosting vessel-like structures in the fundus images, an experimental approach is presented in this paper. The ability of the filter to improve vessel-like structures in fundus images is validated, and an experimental technique for evaluating filter performance using the U-Net model is described. Five quantitative performances, namely Accuracy, Recall, Specificity, Precision, and F1 Score measures on the DRIVE, HRF, DIARETDB1, DIARETDB0, CHASEDB1, ORIGA, DRISHTI GS, DRIONS DB, FIRE, and FIOT datasets, were evaluated and quantified in these experiments to validate the efficacy of the proposed filter. The results demonstrated a noteworthy improvement by achieving an Accuracy of 99.79 %, 99.84 %, 99.64 %, 99.72 %, 99.57 %, 99.48 %, 99.77 %, 99.05 %, 99.93 %, and 99.40 % respectively. Empirical evidence supports the advantages of this approach.

GKE-TUNet: Geometry-Knowledge Embedded TransUNet Model for Retinal Vessel Segmentation Considering Anatomical Topology.

Automated retinal vessel segmentation is crucial for computer-aided clinical diagnosis and retinopathy screening. However, deep learning faces challenges in extracting complex intertwined structures and subtle small vessels from densely vascularized regions. To address these issues, we propose a novel segmentation model, called Geometry-Knowledge Embedded TransUNet (GKE-TUNet), which incorporates explicit embedding of topological features of retinal vessel anatomy. In the proposed GKE-TUNet model, a skeleton extraction network is pre-trained to extract the anatomical topology of retinal vessels from refined segmentation labels. During vessel segmentation, the dense skeleton graph is sampled as a graph of key-points and connections and is incorporated into the skip connection layer of TransUNet. The graph vertices are used as node features and correspond to positions in the low-level feature maps. The graph attention network (GAT) is used as the graph convolution backbone network to capture the shape semantics of vessels and the interaction of key locations along the topological direction. Finally, the node features obtained by graph convolution are read out as a sparse feature map based on their corresponding spatial coordinates. To address the problem of sparse feature maps, we employ convolution operators to fuse sparse feature maps with low-level dense feature maps. This fusion is weighted and connected to deep feature maps. Experimental results on the DRIVE, CHASE-DB1, and STARE datasets demonstrate the competitiveness of our proposed method compared to existing ones.

DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.

Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.

A multi-modal and multi-stage fusion enhancement network for segmentation based on OCT and OCTA images

The accurate segmentation of retinal vessels (RV) and Foveal Avascular Zone (FAZ) is crucial for the retina health assessment. However, fusing OCT and OCTA images while projecting these 3D data for segmentation is a challenge. In this paper, we propose an Adaptive Projection and Multi-stage Fusion Enhancement Network for the segmentation of RV and FAZ, which consists of 3D projection, fusion enhancement, and 2D segmentation parts. We design a kernel adaptive unidirectional convolution to improve projection efficiency and a volume fusion module to capture semantic and spatial information accurately. Then we construct a Multi-stage Complementary Fusion Module and a Multi-modal Difference Fusion Module to enhance features by complementarity and differences in features. At last, we employ three U-Nets with a Feature Interaction Module for segmentation. Experiments show that our method achieves state-of-the-art performance with DICE, BACC, and JAC of 0.8996, 0.8181, 0.9421, 0.9395, 0.8923, and 0.9717 for RV and FAZ.

Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.

Segmentation of retinal fragments like blood vessels, Optic Disc (OD), and Optic Cup (OC) enables the early detection of different retinal pathologies like Diabetic Retinopathy (DR), Glaucoma, etc. Accurate segmentation of OD remains challenging due to blurred boundaries, vessel occlusion, and other distractions and limitations. These days, deep learning is rapidly progressing in the segmentation of image pixels, and a number of network models have been proposed for end-to-end image segmentation. However, there are still certain limitations, such as limited ability to represent context, inadequate feature processing, limited receptive field, etc., which lead to the loss of local details and blurred boundaries. A multi-dimensional dense attention network, or MDDA-Net, is proposed for pixel-wise segmentation of OD in retinal images in order to address the aforementioned issues and produce more thorough and accurate segmentation results. In order to acquire powerful contexts when faced with limited context representation capabilities, a dense attention block is recommended. A triple-attention (TA) block is introduced in order to better extract the relationship between pixels and obtain more comprehensive information, with the goal of addressing the insufficient feature processing. In the meantime, a multi-scale context fusion (MCF) is suggested for acquiring the multi-scale contexts through context improvement. Specifically, we provide a thorough assessment of the suggested approach on three difficult datasets. In the MESSIDOR and ORIGA data sets, the suggested MDDA-NET approach obtains accuracy levels of 99.28% and 98.95%, respectively. The experimental results show that the MDDA-Net can obtain better performance than state-of-the-art deep learning models under the same environmental conditions.

Laddernet retinal vessel segmentation algorithm fusing attentional mechanisms

A retinal blood vessel segmentation algorithm fusing the attention mechanism is proposed to address the problems of insufficient accuracy and incorrect segmentation of minutiae in existing retinal blood vessel segmentation algorithms. The network structure used in this algorithm is Laddernet (LCT-UNet for short), which fuses the Convolutional Block Attention Module (CBAM) and Transformer, i.e., the residual module of Laddernet, which shares the weights, is structurally optimised to decrease the parameter count; After the residual module, CBAM is incorporated to adaptively adjust the weights of channels and spatial features to enhance the feature representation; Transformer with global self-attention mechanism is introduced at the bottom of the network to overcome the shortcomings of UNet's inability to model long-range relationships and spatial dependencies, and gradually refine the features so that the segmentation results have finer boundary and detail information. The model validity was verified on the public datasets DRIVE and CHASEDB1, and the experimental results showed that the ACC of LCT-UNet was 95.81% and 96.88%, SE was 79.74% and 81.12%, SP was 98.15% and 98.83%, and AUC was 98.22% and 98.99%, and the F1 values were 82.88% and 85.14%, respectively, and the comprehensive segmentation performance of the LCT-UNet network showed significant enhancement over the algorithms such as U-Net, R2UNet, and Laddernet.

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

LMBiS-Net: A lightweight bidirectional skip connection based multipath CNN for retinal blood vessel segmentation

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net’s robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.

WITHDRAWN: LSAC-Net: A lightweight scale-aware CNN with densely connected focal modulation for retinal blood vessel segmentation

The diagnosis of sight-threatening eye conditions typically involves segmenting retinal structures in fundus images. The segmentation process enables clinicians to identify alterations in retinal morphology, facilitating the detection and diagnosis of various eye diseases. Despite recent developments in image processing, current approaches often fail to partition delicate arteries precisely. Although deep learning has great potential for segmenting medical images, the accuracy of the segmentation process may be limited due to its dependence on repetitive convolution and pooling processes, which may impair the representation of edge information. In this study, we present LSAC-Net, a pixel-level convolutional neural network (CNN) designed specifically for retinal vessel segmentation. Moreover, it is lightweight, with only 37.5 K learnable parameters. LSAC-Net integrates a unique scale-aware feature extraction block (SAFEB) and a densely connected focal modulation block (DCFMB) to improve segmentation accuracy. In addition, careful selection of filter numbers to avoid overlap effectively shortens training times and boosts computational performance, both of which improve overall model efficiency. We performed extensive tests on different aspects of retinal images to evaluate the robustness and generalizability of LSAC-Net. In particular, a thorough evaluation of the model's precision in retinal artery segmentation, a crucial task for ophthalmological diagnosis and treatment, was carried out. We evaluated the performance and efficacy of LSAC-Net with a particular focus on RBV. The model obtains 98.65% specificity, 97.54% accuracy, and 98.88% area under the curve surpassing existing approaches. Experimental results confirm that LSAC-Net is robust, generalizable, and maintains a high level of segmentation accuracy. These features highlight LSAC-Net's potential as a useful instrument for accurate and timely retinal image segmentation in a variety of therapeutic contexts.

Exploring Generalizable Distillation for Efficient Medical Image Segmentation.

Efficient medical image segmentation aims to provide accurate pixel-wise predictions with a lightweight implementation framework. However, existing lightweight networks generally overlook the generalizability of the cross-domain medical segmentation tasks. In this paper, we propose Generalizable Knowledge Distillation (GKD), a novel framework for enhancing the performance of lightweight networks on cross-domain medical segmentation by generalizable knowledge distillation from powerful teacher networks. Considering the domain gaps between different medical datasets, we propose the Model-Specific Alignment Networks (MSAN) to obtain the domain-invariant representations. Meanwhile, a customized Alignment Consistency Training (ACT) strategy is designed to promote the MSAN training. Based on the domain-invariant vectors in MSAN, we propose two generalizable distillation schemes, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD). In DCGD, two implicit contrastive graphs are designed to model the intra-coupling and inter-coupling semantic correlations. Then, in DICD, the domain-invariant semantic vectors are reconstructed from two networks (i.e., teacher and student) with a crossover manner to achieve simultaneous generalization of lightweight networks, hierarchically. Moreover, a metric named Fréchet Semantic Distance (FSD) is tailored to verify the effectiveness of the regularized domain-invariant features. Extensive experiments conducted on the Liver, Retinal Vessel and Colonoscopy segmentation datasets demonstrate the superiority of our method, in terms of performance and generalization ability on lightweight networks.

Retinal Blood Vessels Segmentation With Improved SE‐UNet Model

ABSTRACTAccurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U‐Net structure. The enhanced U‐Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze‐and‐excitation blocks) to adaptively extract image features after each U‐Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U‐Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance.

A Retinal Vessel Segmentation Network Fusing Cross-Modal Features

A Retinal Vessel Segmentation Network Fusing Cross-Modal Features

A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model.

Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet's generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations.

ARSA-UNet: Atrous residual network based on Structure-Adaptive model for retinal vessel segmentation

Retinal vessel segmentation remains a challenging task due to the myriad morphological characteristics of the retinal vessel and the complexity of the retinal background. To address the challenge, a novel ARSA-UNet network is proposed in this paper. Firstly, a structure adaptive layer is proposed as a substitute for the traditional convolutional layer to enhance the convolution performance. It aims to adapt different feature mappings through model structure adjustment and improve the learning efficiency of the network. Then, the proposed atrous residual path is incorporated in the hopping layer to dynamically adjust the receptive field to obtain deeper multi-scale semantic information and richer spatial information. Finally, a feature-screening fused module is added to the decoder to suppress redundant features, achieving a more effective fusion of upper- and lower-layer information. In addition, to mitigate the network degradation caused by the deepening of layers, a multi-scale deep supervised mechanism is introduced so that the deep network can also be adequately trained. Extensive experiments have been conducted on the mainstream datasets DRIVE, CHASE, and STARE, with results suggesting that the Sen value of the proposed method achieves the optimal results by the metrics set out in all three datasets. Meanwhile, F1, Acc and Spe fare well in most of the datasets. Overall, ARSA-UNet outperforms other state-of-the-art methods for the procedure. Source code is available at https://github.com/LaboratoryofCSBC/SADS-UNet.

TLTNet: A novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation

Extracting global and local feature information is still challenging due to the problems of retinal blood vessel medical images like fuzzy edge features, noise, difficulty in distinguishing between lesion regions and background information, and loss of low-level feature information, which leads to insufficient extraction of feature information. To better solve these problems and fully extract the global and local feature information of the image, we propose a novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation, which consists of an encoder and a decoder and is connected between the encoder and decoder by a transscale transformer cascade module. Among them, the encoder consists of a local–global transscale transformer module, a multi-head layered transscale adaptive embedding module, and a local context(LCNet) module. The transscale transformer cascade module learns local and global feature information from the first three layers of the encoder, and multi-scale dependent features, fuses the hierarchical feature information from the skip connection block and the channel-token interaction fusion block, respectively, and inputs it to the decoder. The decoder includes a decoding module for the local context network and a transscale position transformer module to input the local and global feature information extracted from the encoder with retained key position information into the decoding module and the position embedding transformer module for recovery and output of the prediction results that are consistent with the input feature information. In addition, we propose an improved cross-entropy loss function based on the difference between the deterministic observation samples and the prediction results with the deviation distance, which is validated on the DRIVE and STARE datasets combined with the proposed network model based on the dual transformer structure in this paper, and the segmentation accuracies are 97.26% and 97.87%, respectively. Compared with other state-of-the-art networks, the results show that the proposed network model has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.

Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation

BackgroundThe retinal vasculature, a crucial component of the human body, mirrors various illnesses such as cardiovascular disease, glaucoma, and retinopathy. Accurate segmentation of retinal vessels in funduscopic images is essential for diagnosing and understanding these conditions. However, existing segmentation models often struggle with images from different sources, making accurate segmentation in crossing-source fundus images challenging. MethodsTo address the crossing-source segmentation issues, this paper proposes a novel Multi-level Adversarial Learning and Pseudo-label Denoising-based Self-training Framework (MLAL&PDSF). Expanding on our previously proposed Multiscale Context Gating with Breakpoint and Spatial Dual Attention Network (MCG&BSA-Net), MLAL&PDSF introduces a multi-level adversarial network that operates at both the feature and image layers to align distributions between the target and source domains. Additionally, it employs a distance comparison technique to refine pseudo-labels generated during the self-training process. By comparing the distance between the pseudo-labels and the network predictions, the framework identifies and corrects inaccuracies, thus enhancing the accuracy of the fine vessel segmentation. ResultsWe have conducted extensive validation and comparative experiments on the CHASEDB1, STARE, and HRF datasets to evaluate the efficacy of the MLAL&PDSF. The evaluation metrics included the area under the operating characteristic curve (AUC), sensitivity (SE), specificity (SP), accuracy (ACC), and balanced F-score (F1). The performance results from unsupervised domain adaptive segmentation are remarkable: for DRIVE to CHASEDB1, results are AUC: 0.9806, SE: 0.7400, SP: 0.9737, ACC: 0.9874, and F1: 0.8851; for DRIVE to STARE, results are AUC: 0.9827, SE: 0.7944, SP: 0.9651, ACC: 0.9826, and F1: 0.8326. ConclusionThese results demonstrate the effectiveness and robustness of MLAL&PDSF in achieving accurate segmentation results from crossing-domain retinal vessel datasets. The framework lays a solid foundation for further advancements in cross-domain segmentation and enhances the diagnosis and understanding of related diseases.

Transformer Connections: Improving Segmentation in Blurred Near‐Infrared Blood Vessel Image in Different Depth

AbstractHigh‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

DCNet: A lightweight retinal vessel segmentation network

Retinal vessel segmentation is a crucial focus within the realm of medical image analysis, playing a pivotal role in early disease diagnosis, notably retinopathy. Deep learning has exhibited remarkable segmentation capabilities for retinal blood vessels, leveraging the advantages of contextual feature learning. However, there are still some shortcomings in fine retinal vessel segmentation due to the loss of semantic information due to too many pooling operations or limited receptive fields due to fewer pooling operations. In response to the nuanced balance required for expanding the receptive field while preserving information integrity during multiple downsampling operations, this paper introduces DCNet (Dilated Convolution Net), a novel lightweight three-layer dilated-convolution-based network tailored for retinal blood vessel segmentation. This three-layer architecture autonomously extracts crucial segmentation features from various levels of the feature map. Each layer comprises a dilated convolution Positive Sequence Block (PSB) and a dilated convolution Reverse Sequence Block (RSB). The dilated convolution operation is strategically exploited for its capacity to extend the receptive field, facilitating effective feature information extraction. Simultaneously, to alleviate semantic information loss within the deep network's feature map, this paper proposes the Nonlinear Feature Extraction Module (NFEM) to supplement shallow network feature information. Furthermore, to comprehensively leverage information from various scale features, a Feature Fusion Module (FFM) is introduced for multiscale vascular feature extraction, ultimately enhancing segmentation accuracy. DCNet undergoes rigorous evaluation on four publicly accessible retinal vascular datasets – DRIVE, STARE, CHASE_DB1, and HRF. Experimental results unequivocally demonstrate that DCNet achieves superior segmentation performance with fewer model parameters compared to existing state-of-the-art methods. The code for DCNet can be accessed on the following website: https://github.com/ChunhuiYu1/DCNet.

VascuConNet: an enhanced connectivity network for vascular segmentation.

Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.

Human computer interaction product for infrared thermographic fundus retinal vessels image segmentation using U-Net

BackgroundRetinal vessel segmentation is critical for ocular health assessments. Traditional methods may lack precision, prompting exploration of advanced techniques. U-Net, a deep learning architecture, shows promise in handling the intricate nature of retinal vessel segmentation. MethodologyThis study focuses on the segmentation of thermographic fundus images using the U-Net architecture. A dataset of 125 images, categorized as normal and abnormal, underwent preprocessing, normalization, and augmentation. The U-Net model, with its contracting, bottleneck, and expansive paths, was implemented for accurate segmentation. A handheld thermographic fundus imaging product was introduced, featuring with Human Computer Interaction and user-friendly interface to optimize interaction and streamline the diagnostic process. ResultsThe segmentation accuracy achieved using U-Net stood at a promising 93.5%. Precision, recall, and F1-score metrics were employed for a detailed evaluation, showcasing the model's ability to identify abnormalities while minimizing false positives. The integration of a thermographic fundus imaging product significantly reduced processing time, demonstrating potential clinical utility. Leave-One-Out Cross-Validation affirmed the model's consistency, achieving an overall accuracy of 93.7%. A comparative analysis revealed U-Net's superiority over the Fully Convolutional Network (FCN) by 7%. ConclusionThis study establishes U-Net's efficacy in thermographic fundus image segmentation, offering precision and efficiency enhancements. The proposed imaging product streamlines diagnostics, emphasizing U-Net's superiority over FCN in retinal vessel segmentation, contributing to advanced medical image analysis.