Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.

Exploring a multi-path U-net with probability distribution attention and cascade dilated convolution for precise retinal vessel segmentation in fundus images

While deep learning has become the go-to method for image denoising due to its impressive noise removal Retinal blood vessel segmentation presents several challenges, including limited labeled image data, complex multi-scale vessel structures, and susceptibility to interference from lesion areas. To confront these challenges, this work offers a novel technique that integrates attention mechanisms and a cascaded dilated convolution module (CDCM) within a multi-path U-Net architecture. First, a dual-path U-Net is developed to extract both coarse and fine-grained vessel structures through separate texture and structural branches. A CDCM is integrated to gather multi-scale vessel features, enhancing the model's ability to extract deep semantic features. Second, a boosting algorithm that incorporates probability distribution attention (PDA) within the upscaling blocks is employed. This approach adjusts the probability distribution, increasing the contribution of shallow information, thereby enhancing segmentation performance in complex backgrounds and reducing the risk of overfitting. Finally, the output from the dual-path U-Net is processed through a feature refinement module. This step further refines the vessel segmentation by integrating and extracting relevant features. Results from experiments on three benchmark datasets, including CHASEDB1, DRIVE, and STARE, demonstrate that the proposed method delivers improved segmentation accuracy compared to existing techniques.

General retinal layer segmentation in OCT images via reinforcement constraint

The change of layer thickness of retina is closely associated with the development of ocular diseases such as glaucoma and optic disc drusen. Optical coherence tomography (OCT) is a widely used technology to visualize the lamellar structures of retina. Accurate segmentation of retinal lamellar structures is crucial for diagnosis, treatment, and related research of ocular diseases. However, existing studies have focused on improving the segmentation accuracy, they cannot achieve consistent segmentation performance on different types of datasets, such as retinal OCT images with optic disc and interference of diseases. To this end, a general retinal layer segmentation method is presented in this paper. To obtain more continuous and smoother boundaries, feature enhanced decoding module with reinforcement constraint is proposed, fusing boundary prior and distribution prior, and correcting bias in learning process simultaneously. To enhance the model's perception of the slender retinal structure, position channel attention is introduced, obtaining global dependencies of both space and channel. To handle the imbalanced distribution of retinal OCT images, focal loss is introduced, guiding the model to pay more attention to retinal layers with a smaller proportion. The designed method achieves the state-of-the-art (SOTA) overall performance on five datasets (i.e., MGU, DUKE, NR206, OCTA500 and private dataset).

LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation

The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (https://hz-t3.matpool.com:28765?token=aQjYR4hqMI), revealing real-time performance with no login requirements.

In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade

Background

The application of Artificial Intelligence (AI) in diagnosing retinal diseases represents a significant advancement in ophthalmological research, with the potential to reshape future practices in the field. This study explores the extensive applications and emerging research frontiers of AI in retinal diseases.

Objective

This study aims to uncover the developments and predict future directions of AI research in retinal disease over the past decade.

Methods

This study analyzes AI utilization in retinal disease research through articles, using citation data sourced from the Web of Science (WOS) Core Collection database, covering the period from January 1, 2014, to December 31, 2023. A combination of WOS analyzer, CiteSpace 6.2 R4, and VOSviewer 1.6.19 was used for a bibliometric analysis focusing on citation frequency, collaborations, and keyword trends from an expert perspective.

Results

A total of 2,861 articles across 93 countries or regions were cataloged, with notable growth in article numbers since 2017. China leads with 926 articles, constituting 32% of the total. The United States has the highest h-index at 66, while England has the most significant network centrality at 0.24. Notably, the University of London is the leading institution with 99 articles and shares the highest h-index (25) with University College London. The National University of Singapore stands out for its central role with a score of 0.16. Research primarily spans ophthalmology and computer science, with "network," "transfer learning," and "convolutional neural networks" being prominent burst keywords from 2021 to 2023.

Conclusion

China leads globally in article counts, while the United States has a significant research impact. The University of London and University College London have made significant contributions to the literature. Diabetic retinopathy is the retinal disease with the highest volume of research. AI applications have focused on developing algorithms for diagnosing retinal diseases and investigating abnormal physiological features of the eye. Future research should pivot toward more advanced diagnostic systems for ophthalmic diseases.

An accurate prediction for respiratory diseases using deep learning on bronchoscopy diagnosis images

INTRODUCTION

Bronchoscopy is of great significance in diagnosing and treating respiratory illness. Using deep learning, a diagnostic system for bronchoscopy images can improve the accuracy of tracheal, bronchial, and pulmonary disease diagnoses for physicians and ensure timely pathological or etiological examinations for patients. Improving the diagnostic accuracy of the algorithms remains the key to this technology.

OBJECTIVES

To deal with the problem, we proposed a multiscale attention residual network (MARN) for diagnosing lung conditions through bronchoscopic images. The multiscale convolutional block attention module (MCBAM) was designed to enable accurate focus on lesion regions by enhancing spatial and channel features. Gradient-weighted Class Activation Map (Grad-CAM) was provided to increase the interpretability of diagnostic results.

METHODS

We collected 615 cases from Harbin Medical University Cancer Hospital, including 2900 images. The dataset was partitioned randomly into training sets, validation sets and test sets to update model parameters, evaluate the model's training performance, select network architecture and parameters, and estimate the final model. In addition, we compared MARN with other algorithms. Furthermore, three physicians with different qualifications were invited to diagnose the same test images, and the results were compared to those of the model.

RESULTS

In the dataset of normal and lesion images, our model displayed an accuracy of 97.76% and an AUC of 99.79%. The model recorded 92.26% accuracy and 96.82% AUC for datasets of benign and malignant lesion images, while it achieved 93.10% accuracy and 99.02% AUC for normal, benign, and malignant lesion images.

CONCLUSION

These results demonstrated that our network outperforms other methods in diagnostic performance. The accuracy of our model is roughly the same as that of experienced physicians and the efficiency is much higher than doctors. MARN has great potential for assisting physicians with assessing the bronchoscopic images precisely.

RetVes segmentation: A pseudo-labeling and feature knowledge distillation optimization technique for retinal vessel channel enhancement

Recent advancements in retinal vessel segmentation, which employ transformer-based and domain-adaptive approaches, show promise in addressing the complexity of ocular diseases such as diabetic retinopathy. However, current algorithms face challenges in effectively accommodating domain-specific variations and limitations of training datasets, which fail to represent real-world conditions comprehensively. Manual inspection by specialists remains time-consuming despite technological progress in medical imaging, underscoring the pressing need for automated and robust segmentation techniques. Additionally, these methods have deficiencies in handling unlabeled target sets, requiring extra preprocessing steps and manual intervention, which hinders their scalability and practical application in clinical settings. This research introduces a novel framework that employs semi-supervised domain adaptation and contrastive pre-training to address these limitations. The proposed model effectively learns from target data by implementing a novel pseudo-labeling approach and feature-based knowledge distillation within a temporal convolutional network (TCN) and extracts robust, domain-independent features. This approach enhances cross-domain adaptation, significantly enhancing the model's versatility and performance in clinical settings. The semi-supervised domain adaptation component overcomes the challenges posed by domain shifts, while pseudo-labeling utilizes the data's inherent structure for enhanced learning, which is particularly beneficial when labeled data is scarce. Evaluated on the DRIVE and CHASE_DB1 datasets, which contain clinical fundus images, the proposed model achieves outstanding performance, with accuracy, sensitivity, specificity, and AUC values of 0.9792, 0.8640, 0.9901, and 0.9868 on DRIVE, and 0.9830, 0.9058, 0.9888, and 0.9950 on CHASE_DB1, respectively, outperforming current state-of-the-art vessel segmentation methods. The partitioning of datasets into training and testing sets ensures thorough validation, while extensive ablation studies with thorough sensitivity analysis of the model's parameters and different percentages of labeled data further validate its robustness.

TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation

Accurate segmentation of retinal blood vessels is crucial for enhancing diagnostic efficiency and preventing disease progression. However, the small size and complex structure of retinal blood vessels, coupled with low contrast in corresponding fundus images, pose significant challenges for this task. We propose a novel approach for retinal vessel segmentation, which combines the transformer and convolutional dual-path decoding U-Net (TCDDU-Net). We propose the selective dense connection swin transformer block, which converts the input feature map into patches, introduces MLPs to generate probabilities, and performs selective fusion at different stages. This structure forms a dense connection framework, enabling the capture of long-distance dependencies and effective fusion of features across different stages. The subsequent stage involves the design of the background decoder, which utilizes deformable convolution to learn the background information of retinal vessels by treating them as segmentation objects. This is then combined with the foreground decoder to form a dual-path decoding U-Net. Finally, the foreground segmentation results and the processed background segmentation results are fused to obtain the final retinal vessel segmentation map. To evaluate the effectiveness of our method, we performed experiments on the DRIVE, STARE, and CHASE datasets for retinal vessel segmentation. Experimental results show that the segmentation accuracies of our algorithms are 96.98, 97.40, and 97.23, and the AUC metrics are 98.68, 98.56, and 98.50, respectively.In addition, we evaluated our methods using F1 score, specificity, and sensitivity metrics. Through a comparative analysis, we found that our proposed TCDDU-Net method effectively improves retinal vessel segmentation performance and achieves impressive results on multiple datasets compared to existing methods.

Vein segmentation and visualization of upper and lower extremities using convolution neural network

OBJECTIVES

The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments.

METHODS

A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization.

RESULTS

Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions.

CONCLUSIONS

Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Efficient skin lesion segmentation with boundary distillation

Medical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge computing devices and compromising efficiency in the segmentation process. In light of this, the industry has proposed the adoption of knowledge distillation techniques. Nevertheless, the vast majority of existing knowledge distillation methods are focused on the classification tasks of skin diseases. Specifically, for the segmentation tasks of dermoscopy lesion images, these knowledge distillation methods fail to fully recognize the importance of features in the boundary regions of lesions within medical images, lacking boundary awareness for skin lesions. This paper introduces pioneering medical image knowledge distillation architecture. The aim of this method is to facilitate the efficient transfer of knowledge from existing complex medical image segmentation networks to a more simplified student network. Initially, a masked boundary feature (MBF) distillation module is designed. By applying random masking to the periphery of skin lesions, the MBF distillation module obliges the student network to reproduce the comprehensive features of the teacher network. This process, in turn, augments the representational capabilities of the student network. Building on the MBF distillation module, this paper employs a cascaded combination approach to integrate the MBF distillation module into a multi-head boundary feature (M2BF) distillation module, further strengthening the student network's feature learning ability and enhancing the overall image segmentation performance of the distillation model. This method has been experimentally validated on the public datasets ISIC-2016 and PH2, with results showing significant performance improvements in the student network. Our findings highlight the practical utility of the lightweight network distilled using our approach, particularly in scenarios demanding high operational speed and minimal storage usage. This research offers promising prospects for practical applications in the realm of medical image segmentation.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images

BACKGROUND

In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment.

METHODS

A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction.

RESULTS

A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%.

CONCLUSION

Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation

Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.

LDMRes-Net: A Lightweight Neural Network for Efficient Medical Image Segmentation on IoT and Edge Devices

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based convolutional neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. In this study, we present the Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net), which is specifically designed to overcome these difficulties. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072 M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multiscale residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on the segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

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.

CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation

Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricate vessel tree in fundus images, which has a significant number of tiny vessels, low contrast, and lesion interference. For this task, the u-shaped architecture (U-Net) has become the de-facto standard and has achieved considerable success. However, U-Net is a pure convolutional network, which usually shows limitations in global modelling. In this paper, we propose a novel Cross-scale U-Net with Semantic-position Dependencies (CS-UNet) for retinal vessel segmentation. In particular, we first designed a Semantic-position Dependencies Aggregator (SPDA) and incorporate it into each layer of the encoder to better focus on global contextual information by integrating the relationship of semantic and position. To endow the model with the capability of cross-scale interaction, the Cross-scale Relation Refine Module (CSRR) is designed to dynamically select the information associated with the vessels, which helps guide the up-sampling operation. Finally, we have evaluated CS-UNet on three public datasets: DRIVE, CHASE_DB1, and STARE. Compared to most existing state-of-the-art methods, CS-UNet demonstrated better performance.

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.

A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion

The incidence of blinding eye diseases is highly correlated with changes in retinal morphology, and is clinically detected by segmenting retinal structures in fundus images. However, some existing methods have limitations in accurately segmenting thin vessels. In recent years, deep learning has made a splash in the medical image segmentation, but the lack of edge information representation due to repetitive convolution and pooling, limits the final segmentation accuracy. To this end, this paper proposes a pixel-level retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion. Here, a multiple dimension attention enhancement (MDAE) block is proposed to acquire more local edge information. Meanwhile, a deep guidance fusion (DGF) block and a cross-pooling semantic enhancement (CPSE) block are proposed simultaneously to acquire more global contexts. Further, the predictions of different decoding stages are learned and aggregated by an adaptive weight learner (AWL) unit to obtain the best weights for effective feature fusion. The experimental results on three public fundus image datasets show that proposed network could effectively enhance the segmentation performance on retinal blood vessels. In particular, the proposed method achieves AUC of 98.30%, 98.75%, and 98.71% on the DRIVE, CHASE_DB1, and STARE datasets, respectively, while the F1 score on all three datasets exceeded 83%. The source code of the proposed model is available at https://github.com/gegao310/VesselSeg-Pytorch-master.

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network

BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work's experimental results achieve high values of accuracy when compared to other existing models.

CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation

Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular pathologies. Current methods have limitations in feature fusion and face challenges in simultaneously capturing global and local features from fundus images. To address these issues, this study introduces a hybrid network named CoVi-Net, which combines convolutional neural networks and vision transformer. In our proposed model, we have integrated a novel module for local and global feature aggregation (LGFA). This module facilitates remote information interaction while retaining the capability to effectively gather local information. In addition, we introduce a bidirectional weighted feature fusion module (BWF). Recognizing the variations in semantic information across layers, we allocate adjustable weights to different feature layers for adaptive feature fusion. BWF employs a bidirectional fusion strategy to mitigate the decay of effective information. We also incorporate horizontal and vertical connections to enhance feature fusion and utilization across various scales, thereby improving the segmentation of multiscale vessel images. Furthermore, we introduce an adaptive lateral feature fusion (ALFF) module that refines the final vessel segmentation map by enriching it with more semantic information from the network. In the evaluation of our model, we employed three well-established retinal image databases (DRIVE, CHASEDB1, and STARE). Our experimental results demonstrate that CoVi-Net outperforms other state-of-the-art techniques, achieving a global accuracy of 0.9698, 0.9756, and 0.9761 and an area under the curve of 0.9880, 0.9903, and 0.9915 on DRIVE, CHASEDB1, and STARE, respectively. We conducted ablation studies to assess the individual effectiveness of the three modules. In addition, we examined the adaptability of our CoVi-Net model for segmenting lesion images. Our experiments indicate that our proposed model holds promise in aiding the diagnosis of retinal vascular disorders.

MAFE-Net: retinal vessel segmentation based on a multiple attention-guided fusion mechanism and ensemble learning network

The precise and automatic recognition of retinal vessels is of utmost importance in the prevention, diagnosis and assessment of certain eye diseases, yet it brings a nontrivial uncertainty for this challenging detection mission due to the presence of intricate factors, such as uneven and indistinct curvilinear shapes, unpredictable pathological deformations, and non-uniform contrast. Therefore, we propose a unique and practical approach based on a multiple attention-guided fusion mechanism and ensemble learning network (MAFE-Net) for retinal vessel segmentation. In conventional UNet-based models, long-distance dependencies are explicitly modeled, which may cause partial scene information loss. To compensate for the deficiency, various blood vessel features can be extracted from retinal images by using an attention-guided fusion module. In the skip connection part, a unique spatial attention module is applied to remove redundant and irrelevant information; this structure helps to better integrate low-level and high-level features. The final step involves a DropOut layer that removes some neurons randomly to prevent overfitting and improve generalization. Moreover, an ensemble learning framework is designed to detect retinal vessels by combining different deep learning models. To demonstrate the effectiveness of the proposed model, experimental results were verified in public datasets STARE, DRIVE, and CHASEDB1, which achieved F1 scores of 0.842, 0.825, and 0.814, and Accuracy values of 0.975, 0.969, and 0.975, respectively. Compared with eight state-of-the-art models, the designed model produces satisfactory results both visually and quantitatively.

Retinal vessel segmentation method based on RSP-SA Unet network

Segmenting retinal vessels plays a significant role in the diagnosis of fundus disorders. However, there are two problems in the retinal vessel segmentation methods. First, fine-grained features of fine blood vessels are difficult to be extracted. Second, it is easy to lose track of the details of blood vessel edges. To solve the problems above, the Residual SimAM Pyramid-Spatial Attention Unet (RSP-SA Unet) is proposed, in which the encoding, decoding, and upsampling layers of the Unet are mainly improved. Firstly, the RSP structure proposed in this paper approximates a residual structure combined with SimAM and Pyramid Segmentation Attention (PSA), which is applied to the encoding and decoding parts to extract multi-scale spatial information and important features across dimensions at a finer level. Secondly, the spatial attention (SA) is used in the upsampling layer to perform multi-attention mapping on the input feature map, which could enhance the segmentation effect of small blood vessels with low contrast. Finally, the RSP-SA Unet is verified on the CHASE_DB1, DRIVE, and STARE datasets, and the segmentation accuracy (ACC) of the RSP-SA Unet could reach 0.9763, 0.9704, and 0.9724, respectively. Area under the ROC curve (AUC) could reach 0.9896, 0.9858, and 0.9906, respectively. The RSP-SA Unet overall performance is better than the comparison methods.

An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation

The segmentation results of retinal blood vessels are crucial for automatically diagnosing ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular diseases. To improve the accuracy of vessel segmentation and better extract information about small vessels and edges, we introduce the U-Net algorithm with a supervised attention mechanism for retinal vessel segmentation. We achieve this by introducing a decoder fusion module (DFM) in the encoding part, effectively combining different convolutional blocks to extract features comprehensively. Additionally, in the decoding part of U-Net, we propose the context squeeze and excitation (CSE) decoding module to enhance important contextual feature information and the detection of tiny blood vessels. For the final output, we introduce the supervised fusion mechanism (SFM), which combines multiple branches from shallow to deep layers, effectively fusing multi-scale features and capturing information from different levels, fully integrating low-level and high-level features to improve segmentation performance. Our experimental results on the public datasets of DRIVE, STARE, and CHASED_B1 demonstrate the excellent performance of our proposed network.

Retina image segmentation using the three-path Unet model

Unsupervised image segmentation is a technique that divides an image into distinct regions or objects without prior labeling. This approach offers flexibility and adaptability to various types of image data. Particularly for large datasets, it eliminates the need for manual labeling, thereby it presents advantages in terms of time and labor costs. However, when applied to retinal image segmentation, challenges arise due to variations in data, presence of noise, and manual threshold adjustments, which can lead to over-segmentation or under-segmentation of small blood vessel boundaries and endpoints. In order to enhance the precision and accuracy of retinal image segmentation, we propose a novel image supervised segmentation network based on three-path Unet model.Firstly, the Haar wavelet transform is employed to extract high-frequency image information, which forms the foundation for the proposed HaarNet, a Unet-inspired architecture. Next, the HaarNet is integrated with the Unet and SegNet frameworks to develop a three-path Unet model, referred to as TP-Unet. Finally, the model is further refined into TP-Unet+AE+DSL by incorporating the advantages of auto-encoding (AE) and deep supervised learning (DSL) techniques, thereby enhancing the overall performance of the system. To evaluate the effectiveness of our proposed model, we conduct experiments using the DRIVE and CHASE public datasets. On the DRIVE dataset, our recommended model achieves a Dice coefficient of 0.8291 and a sensitivity index of 0.8184. These results significantly outperform the Unet model by [Formula: see text] and [Formula: see text], respectively. Furthermore, our model demonstrates excellent performance on the CHASE dataset, with a Dice coefficient of 0.8162, a sensitivity of 0.8242, and an accuracy of 0.9664. These metrics surpass the Unet model by [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Our proposed model provides more accurate and reliable results for retinal vessel segmentation, which holds significant potential for assisting doctors in their diagnosis.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

A High-Resolution Network with Strip Attention for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels is an essential prerequisite for the subsequent analysis of fundus images. Recently, a number of methods based on deep learning have been proposed and shown to demonstrate promising segmentation performance, especially U-Net and its variants. However, tiny vessels and low-contrast vessels are hard to detect due to the issues of a loss of spatial details caused by consecutive down-sample operations and inadequate fusion of multi-level features caused by vanilla skip connections. To address these issues and enhance the segmentation precision of retinal vessels, we propose a novel high-resolution network with strip attention. Instead of the U-Net-shaped architecture, the proposed network follows an HRNet-shaped architecture as the basic network, learning high-resolution representations throughout the training process. In addition, a strip attention module including a horizontal attention mechanism and a vertical attention mechanism is designed to obtain long-range dependencies in the horizontal and vertical directions by calculating the similarity between each pixel and all pixels in the same row and the same column, respectively. For effective multi-layer feature fusion, we incorporate the strip attention module into the basic network to dynamically guide adjacent hierarchical features. Experimental results on the DRIVE and STARE datasets show that the proposed method can extract more tiny vessels and low-contrast vessels compared with existing mainstream methods, achieving accuracies of 96.16% and 97.08% and sensitivities of 82.68% and 89.36%, respectively. The proposed method has the potential to aid in the analysis of fundus images.

SURVS: A Swin-Unet and game theory-based unsupervised segmentation method for retinal vessel

Medical images, especially intricate vascular structures, are costly and time-consuming to annotate manually. It is beneficial to investigate an unsupervised method for vessel segmentation, one that circumvents the manual annotation yet remains valuable for disease detection. In this study, we design an unsupervised retinal vessel segmentation model based on the Swin-Unet framework and game theory. First, we construct two extreme pseudo-mapping functions by changing the contrast of the images and obtain their corresponding pseudo-masks based the on binary segmentation method and mathematical morphology, then we prove that there exists a mapping function between pseudo-mappings such that its corresponding mask is closest to the ground true mask. To acquire the best-predicted mask, based on which, we second develop a model based on the Swin-Unet frame to solve the optimal mapping function, and introduce an Image Colorization proxy task to assist the learning of pixel-level feature representations. Third, since to the instability of two pseudo-masks, the predicted mask will inevitably have errors, inspired by the two-player, non-zero-sum, non-cooperative Neighbor's Collision game in game theory, a game filter is proposed in this paper to reduce the errors in the final predicted mask. Finally, we verify the effectiveness of the presented unsupervised retinal vessel segmentation model on DRIVE, STARE and CHASE_DB1 datasets, and extensive experiments show that has obvious advantages over image segmentation and conventional unsupervised models.

Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters.

Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules

BACKGROUND

To develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images.

METHODS

Including 313 patients aged 16 - 65 years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation.

RESULTS

The performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886.

CONCLUSION

We propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.

CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation

Precise segmentation of retinal vessels plays an important role in computer-assisted diagnosis. Deep learning models have been applied to retinal vessel segmentation, but the efficacy is limited by the significant scale variation of vascular structures and the intricate background of retinal images. This paper supposes a cross-channel spatial attention U-Net (CCS-UNet) for accurate retinal vessel segmentation. In comparison to other models based on U-Net, our model employes a ResNeSt block for the encoder-decoder architecture. The block has a multi-branch structure that enables the model to extract more diverse vascular features. It facilitates weight distribution across channels through the incorporation of soft attention, which effectively aggregates contextual information in vascular images. Furthermore, we suppose an attention mechanism within the skip connection. This mechanism serves to enhance feature integration across various layers, thereby mitigating the degradation of effective information. It helps acquire cross-channel information and enhance the localization of regions of interest, ultimately leading to improved recognition of vascular structures. In addition, the feature fusion module (FFM) module is used to provide semantic information for a more refined vascular segmentation map. We evaluated CCS-UNet based on five benchmark retinal image datasets, DRIVE, CHASEDB1, STARE, IOSTAR and HRF. Our proposed method exhibits superior segmentation efficacy compared to other state-of-the-art techniques with a global accuracy of 0.9617/0.9806/0.9766/0.9786/0.9834 and AUC of 0.9863/0.9894/0.9938/0.9902/0.9855 on DRIVE, CHASEDB1, STARE, IOSTAR and HRF respectively. Ablation studies are also performed to evaluate the the relative contributions of different architectural components. Our proposed model is potential for diagnostic aid of retinal diseases.

Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation

Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus diseases. However, recent methods generally neglect the difference of semantic information between deep and shallow features, which fail to capture the global and local characterizations in fundus images simultaneously, resulting in the limited segmentation performance for fine vessels. In this article, a global transformer (GT) and dual local attention (DLA) network via deep-shallow hierarchical feature fusion (GT-DLA-dsHFF) are investigated to solve the above limitations. First, the GT is developed to integrate the global information in the retinal image, which effectively captures the long-distance dependence between pixels, alleviating the discontinuity of blood vessels in the segmentation results. Second, DLA, which is constructed using dilated convolutions with varied dilation rates, unsupervised edge detection, and squeeze-excitation block, is proposed to extract local vessel information, consolidating the edge details in the segmentation result. Finally, a novel deep-shallow hierarchical feature fusion (dsHFF) algorithm is studied to fuse the features in different scales in the deep learning framework, respectively, which can mitigate the attenuation of valid information in the process of feature fusion. We verified the GT-DLA-dsHFF on four typical fundus image datasets. The experimental results demonstrate our GT-DLA-dsHFF achieves superior performance against the current methods and detailed discussions verify the efficacy of the proposed three modules. Segmentation results of diseased images show the robustness of our proposed GT-DLA-dsHFF. Implementation codes will be available on https://github.com/YangLibuaa/GT-DLA-dsHFF.

SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation

Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkable success in medical image segmentation, they still struggle in the subfield of subcutaneous vessel segmentation due to the scarcity and low-quality of annotated data. To relieve it, this work presents a novel semi-supervised learning framework, SCANet, that achieves accurate vessel segmentation through an alternate training strategy. The SCANet is composed of a multi-scale recurrent neural network that embeds coarse-to-fine features and two auxiliary branches, a consistency decoder and an adversarial learning branch, responsible for strengthening fine-grained details and eliminating differences between ground-truths and predictions, respectively. Equipped with a novel semi-supervised alternate training strategy, the three components work collaboratively, enabling SCANet to accurately segment vessel regions with only a handful of labeled data and abounding unlabeled data. Moreover, to mitigate the shortage of annotated data in this field, we provide a new subcutaneous vessel dataset, VESSEL-NIR. Extensive experiments on a wide variety of tasks, including the segmentation of subcutaneous vessels, retinal vessels, and skin lesions, well demonstrate the superiority and generality of our approach.

SDMT: Spatial Dependence Multi-Task Transformer Network for 3D Knee MRI Segmentation and Landmark Localization

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon's clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

A lightweight dual-path cascaded network for vessel segmentation in fundus image

Automatic and fast segmentation of retinal vessels in fundus images is a prerequisite in clinical ophthalmic diseases; however, the high model complexity and low segmentation accuracy still limit its application. This paper proposes a lightweight dual-path cascaded network (LDPC-Net) for automatic and fast vessel segmentation. We designed a dual-path cascaded network via two U-shaped structures. Firstly, we employed a structured discarding (SD) convolution module to alleviate the over-fitting problem in both codec parts. Secondly, we introduced the depthwise separable convolution (DSC) technique to reduce the parameter amount of the model. Thirdly, a residual atrous spatial pyramid pooling (ResASPP) model is constructed in the connection layer to aggregate multi-scale information effectively. Finally, we performed comparative experiments on three public datasets. Experimental results show that the proposed method achieved superior performance on the accuracy, connectivity, and parameter quantity, thus proving that it can be a promising lightweight assisted tool for ophthalmic diseases.

TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF

Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHASE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation

BACKGROUND

Accurate and automated brain tumor segmentation from multi-modality MR images plays a significant role in tumor treatment. However, the existing approaches mainly focus on the fusion of multi-modality while ignoring the correlation between single-modality and tumor subcomponents. For example, T2-weighted images show good visualization of edema, and T1-contrast images have a good contrast between enhancing tumor core and necrosis. In the actual clinical process, professional physicians also label tumors according to these characteristics. We design a method for brain tumors segmentation that utilizes both multi-modality fusion and single-modality characteristics.

METHODS

A multi-modality and single-modality feature recalibration network (MSFR-Net) is proposed for brain tumor segmentation from MR images. Specifically, multi-modality information and single-modality information are assigned to independent pathways. Multi-modality network explicitly learns the relationship between all modalities and all tumor sub-components. Single-modality network learns the relationship between single-modality and its highly correlated tumor subcomponents. Then, a dual recalibration module (DRM) is designed to connect the parallel single-modality network and multi-modality network at multiple stages. The function of the DRM is to unify the two types of features into the same feature space.

RESULTS

Experiments on BraTS 2015 dataset and BraTS 2018 dataset show that the proposed method is competitive and superior to other state-of-the-art methods. The proposed method achieved the segmentation results with Dice coefficients of 0.86 and Hausdorff distance of 4.82 on BraTS 2018 dataset, with dice coefficients of 0.80, positive predictive value of 0.76, and sensitivity of 0.78 on BraTS 2015 dataset.

CONCLUSIONS

This work combines the manual labeling process of doctors and introduces the correlation between single-modality and the tumor subcomponents into the segmentation network. The method improves the segmentation performance of brain tumors and can be applied in the clinical practice. The code of the proposed method is available at: https://github.com/xiangQAQ/MSFR-Net.

TP-Net: Two-Path Network for Retinal Vessel Segmentation

Refined and automatic retinal vessel segmentation is crucial for computer-aided early diagnosis of retinopathy. However, existing methods often suffer from mis-segmentation when dealing with thin and low-contrast vessels. In this paper, a two-path retinal vessel segmentation network is proposed, namely TP-Net, which consists of three core parts, i.e., main-path, sub-path, and multi-scale feature aggregation module (MFAM). Main-path is to detect the trunk area of the retinal vessels, and the sub-path to effectively capture edge information of the retinal vessels. The prediction results of the two paths are combined by MFAM, obtaining refined segmentation of retinal vessels. In the main-path, a three-layer lightweight backbone network is elaborately designed according to the characteristics of retinal vessels, and then a global feature selection mechanism (GFSM) is proposed, which can autonomously select features that are more important for the segmentation task from the features at different layers of the network, thereby, enhancing the segmentation capability for low-contrast vessels. In the sub-path, an edge feature extraction method and an edge loss function are proposed, which can enhance the ability of the network to capture edge information and reduce the mis-segmentation of thin vessels. Finally, MFAM is proposed to fuse the prediction results of main-path and sub-path, which can remove background noises while preserving edge details, and thus, obtaining refined segmentation of retinal vessels. The proposed TP-Net has been evaluated on three public retinal vessel datasets, namely DRIVE, STARE, and CHASE DB1. The experimental results show that the TP-Net achieved a superior performance and generalization ability with fewer model parameters compared with the state-of-the-art methods.

MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading

BACKGROUND

The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area.

PURPOSE

In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis.

METHODS

The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade.

RESULTS

The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%.

CONCLUSIONS

The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.

Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method

Automatic retinal blood vessel segmentation is a key link in the diagnosis of ophthalmic diseases. Recent deep learning methods have achieved high accuracy in vessel segmentation but still face challenges in maintaining vascular structural connectivity. Therefore, this paper proposes a novel retinal blood vessel segmentation strategy that includes three stages: vessel structure detection, vessel branch extraction and broken vessel segment reconnection. First, we propose a multiscale linear structure detection network (MS-LSDNet), which improves the detection ability of fine blood vessels by learning the types of rich hierarchical features. In addition, to maintain the connectivity of the vascular structure in the process of binarization of the vascular probability map, an adaptive hysteresis threshold method for vascular extraction is proposed. Finally, we propose a vascular tree structure reconstruction algorithm based on a geometric skeleton to connect the broken vessel segments. Experimental results on three publicly available datasets show that compared with current state-of-the-art algorithms, our strategy effectively maintains the connectivity of retinal vascular tree structure.

Feature preserving mesh network for semantic segmentation of retinal vasculature to support ophthalmic disease analysis

Introduction

Ophthalmic diseases are approaching an alarming count across the globe. Typically, ophthalmologists depend on manual methods for the analysis of different ophthalmic diseases such as glaucoma, Sickle cell retinopathy (SCR), diabetic retinopathy, and hypertensive retinopathy. All these manual assessments are not reliable, time-consuming, tedious, and prone to error. Therefore, automatic methods are desirable to replace conventional approaches. The accuracy of this segmentation of these vessels using automated approaches directly depends on the quality of fundus images. Retinal vessels are assumed as a potential biomarker for the diagnosis of many ophthalmic diseases. Mostly newly developed ophthalmic diseases contain minor changes in vasculature which is a critical job for the early detection and analysis of disease.

Method

Several artificial intelligence-based methods suggested intelligent solutions for automated retinal vessel detection. However, existing methods exhibited significant limitations in segmentation performance, complexity, and computational efficiency. Specifically, most of the existing methods failed in detecting small vessels owing to vanishing gradient problems. To overcome the stated problems, an intelligence-based automated shallow network with high performance and low cost is designed named Feature Preserving Mesh Network (FPM-Net) for the accurate segmentation of retinal vessels. FPM-Net employs a feature-preserving block that preserves the spatial features and helps in maintaining a better segmentation performance. Similarly, FPM-Net architecture uses a series of feature concatenation that also boosts the overall segmentation performance. Finally, preserved features, low-level input image information, and up-sampled spatial features are aggregated at the final concatenation stage for improved pixel prediction accuracy. The technique is reliable since it performs better on the DRIVE database, CHASE-DB1 database, and STARE dataset.

Results and discussion

Experimental outcomes confirm that FPM-Net outperforms state-of-the-art techniques with superior computational efficiency. In addition, presented results are achieved without using any preprocessing or postprocessing scheme. Our proposed method FPM-Net gives improvement results which can be observed with DRIVE datasets, it gives Se, Sp, and Acc as 0.8285, 0.98270, 0.92920, for CHASE-DB1 dataset 0.8219, 0.9840, 0.9728 and STARE datasets it produces 0.8618, 0.9819 and 0.9727 respectively. Which is a remarkable difference and enhancement as compared to the conventional methods using only 2.45 million trainable parameters.

Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images

Accurate segmentation of retinal vessels from fundus images is fundamental for the diagnosis of numerous diseases of eye, and an automated vessel segmentation method can effectively help clinicians to make accurate diagnosis for the patients and provide the appropriate treatment schemes. It is important to note that both thick and thin vessels play the key role for disease judgements. Because of complex factors, the precise segmentation of thin vessels is still a great challenge, such as the presence of various lesions, image noise, complex backgrounds and poor contrast in the fundus images. Recently, because of the advantage of context feature representation learning capabilities, deep learning has shown a remarkable segmentation performance on retinal vessels. However, it still has some shortcomings on high-precision retinal vessel extraction due to some factors, such as semantic information loss caused by pooling operations, limited receptive field, etc. To address these problems, this paper proposes a new lightweight segmentation network for precise retinal vessel segmentation, which is called as Wave-Net model on account of the whole shape. To alleviate the influence of semantic information loss problem to thin vessels, to acquire more contexts about micro structures and details, a detail enhancement and denoising block (DED) is proposed to improve the segmentation precision on thin vessels, which replaces the simple skip connections of original U-Net. On the other hand, it could well alleviate the influence of the semantic gap problem. Further, faced with limited receptive field, for multi-scale vessel detection, a multi-scale feature fusion block (MFF) is proposed to fuse cross-scale contexts to achieve higher segmentation accuracy and realize effective processing of local feature maps. Experiments indicate that proposed Wave-Net achieves an excellent performance on retinal vessel segmentation while maintaining a lightweight network design compared to other advanced segmentation methods, and it also has shown a better segmentation ability to thin vessels.

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

As an important basis of clinical diagnosis, the morphology of retinal vessels is very useful for the early diagnosis of some eye diseases. In recent years, with the rapid development of deep learning technology, automatic segmentation methods based on it have made considerable progresses in the field of retinal blood vessel segmentation. However, due to the complexity of vessel structure and the poor quality of some images, retinal vessel segmentation, especially the segmentation of Capillaries, is still a challenging task. In this work, we propose a new retinal blood vessel segmentation method, called multi-feature segmentation, based on collaborative patches. First, we design a new collaborative patch training method which effectively compensates for the pixel information loss in the patch extraction through information transmission between collaborative patches. Additionally, the collaborative patch training strategy can simultaneously have the characteristics of low occupancy, easy structure and high accuracy. Then, we design a multi-feature network to gather a variety of information features. The hierarchical network structure, together with the integration of the adaptive coordinate attention module and the gated self-attention module, enables these rich information features to be used for segmentation. Finally, we evaluate the proposed method on two public datasets, namely DRIVE and STARE, and compare the results of our method with those of other nine advanced methods. The results show that our method outperforms other existing methods.