Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function

(DA-U)2Net: double attention U2Net for retinal vessel segmentation

BACKGROUND

Morphological changes in the retina are crucial and serve as valuable references in the clinical diagnosis of ophthalmic and cardiovascular diseases. However, the retinal vascular structure is complex, making manual segmentation time-consuming and labor-intensive.

METHODS

This paper proposes a retinal segmentation network that integrates feature channel attention and the Convolutional Block Attention Module (CBAM) attention within the U2Net model. First, a feature channel attention module is introduced into the RSU (Residual Spatial Unit) block of U2Net, forming an Attention-RSU block, which focuses more on significant areas during feature extraction and suppresses the influence of noise; Second, a Spatial Attention Module (SAM) is introduced into the high-resolution module of Attention-RSU to enrich feature extraction from both spatial and channel dimensions, and a Channel Attention Module (CAM) is integrated into the lowresolution module of Attention-RSU, which uses dual channel attention to reduce detail loss.Finally, dilated convolution is applied during the upscaling and downscaling processes to expand the receptive field in low-resolution states, allowing the model to better integrate contextual information.

RESULTS

The evaluation across multiple clinical datasets demonstrated excellent performance on various metrics, with an accuracy (ACC) of 98.71%.

CONCLUSION

The proposed Network is general enough and we believe it can be easily extended to other medical image segmentation tasks where large scale variation and complicated features are the main challenges.

Multi scale multi attention network for blood vessel segmentation in fundus images

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB). Our experimental findings on publicly available datasets of fundus images, specifically DRIVE, STARE, CHASE_DB1, HRF and DR HAGIS consistently demonstrate that our approach outperforms other segmentation techniques, achieving higher accuracy, sensitivity, Dice score, and area under the receiver operator characteristic (AUC) in the segmentation of blood vessels with different thicknesses, even in situations involving diverse contextual information, the presence of coexisting lesions, and intricate vessel morphologies.

Intraoperative Augmented Reality for Vitreoretinal Surgery Using Edge Computing

Purpose: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available. Methods: This study is a retrospective analysis involving the development and testing of a novel image-registration algorithm for vitreoretinal surgery. Fifteen anonymized cases of pars plana vitrectomy with epiretinal membrane peeling, along with corresponding preoperative fundus photographs and optical coherence tomography (OCT) images, were retrospectively collected from the Mayo Clinic database. We developed a TPU (Tensor-Processing Unit)-accelerated CNN for semantic segmentation of retinal vessels from fundus photographs and subsequent real-time image registration in surgical video streams. An iterative patch-wise cross-correlation (IPCC) algorithm was developed for image registration, with a focus on optimizing processing speeds and maintaining high spatial accuracy. The primary outcomes measured were processing speed in frames per second (FPS) and the spatial accuracy of image registration, quantified by the Dice coefficient between registered and manually aligned images. Results: When deployed on an Edge TPU, the CNN model combined with our image-registration algorithm processed video streams at a rate of 14 FPS, which is superior to processing rates achieved on other standard hardware configurations. The IPCC algorithm efficiently aligned pre-operative and intraoperative images, showing high accuracy in comparison to manual registration. Conclusions: This study demonstrates the feasibility of using TPU-accelerated CNNs for enhanced AR in vitreoretinal surgery.

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation

Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonable strategy to explore the discriminative semantics of multi-scale ICH regions, making it difficult to address the challenge of complex morphology in clinical data. In this paper, we propose a novel multi-scale object equalization learning network (MOEL-Net) for accurate ICH region segmentation. Specifically, we first introduce a shallow feature extraction module (SFEM) for obtaining shallow semantic representations to maintain sufficient and effective detailed location information. Then, a deep feature extraction module (DFEM) is leveraged to extract the deep semantic information of the ICH region from the combination of SFEM and original image features. To further achieve equalization learning in different scales of ICH regions, we introduce a multi-level semantic feature equalization fusion module (MSFEFM), which explores the equalized fusion features of the described objects with the assistance of shallow and deep semantic information provided by SFEM and DFEM. Driven by the above three designs, MOEL-Net shows a solid capacity to capture more discriminative features in various ICH region segmentation. To promote the research of clinical automatic ICH region segmentation, we collect two datasets, VMICH and FRICH (divided into Test A and Test B) for evaluation. Experimental results show that the proposed model achieves the Dice scores of 88.28%, 90.92%, and 90.95% on the VMICH, FRICH Test A, and Test B, respectively, which outperform fourteen competing methods.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

An accurate unsupervised extraction of retinal vasculature using curvelet transform and classical morphological operators

BACKGROUND

Many ophthalmic disorders such as diabetic retinopathy and hypertension can be early diagnosed by analyzing changes related to the vascular structure of the retina. Accuracy and efficiency of the segmentation of retinal blood vessels are important parameters that can help the ophthalmologist to better characterize the targeted anomalies.

METHOD

In this work, we propose a new method for accurate unsupervised automatic segmentation of retinal blood vessels based on a simple and adequate combination of classical filters. Initially, contrast of vessels in retinal image is significantly improved by adding the Curvelet Transform to commonly used Contrast-Limited Adaptive Histogram Equalization technique. Afterwards, a morphological operator using Top Hat is applied to highlight vascular network. Then, a global threshold-based Otsu technique using minimum of intra-class variance is applied for vessel detection. Finally, a cleanup operation based on Match Filter and First Derivative Order Gaussian with fixed parameters is used to remove unwanted or isolated segments. We test the proposed method on images from two publicly available STARE and DRIVE databases.

RESULTS

We achieve in terms of sensitivity, specificity and accuracy the respective average performances of 0.7407, 0.9878 and 0.9667 on the DRIVE database, then 0.7028, 0.9755 and 0.9507 on the STARE database.

CONCLUSIONS

Compared to some recent similar work, the obtained results are quite promising and can thus contribute to the optimization of automatic tools to aid in the diagnosis of eye disorders.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality

BACKGROUND

Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications.

METHODS

The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival.

RESULTS

The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001).

CONCLUSIONS

Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

SEG-LUS: A novel ultrasound segmentation method for liver and its accessory structures based on muti-head self-attention

Although liver ultrasound (US) is quick and convenient, it presents challenges due to patient variations. Previous research has predominantly focused on computer-aided diagnosis (CAD), particularly for disease analysis. However, characterizing liver US images is complex due to structural diversity and a limited number of samples. Normal liver US images are crucial, especially for standard section diagnosis. This study explicitly addresses Liver US standard sections (LUSS) and involves detailed labeling of eight anatomical structures. We propose SEG-LUS, a US image segmentation model for the liver and its accessory structures. In SEG-LUS, we have adopted the shifted windows feature encoder combined with the cross-attention mechanism to adapt to capturing image information at different scales and resolutions and address context mismatch and sample imbalance in the segmentation task. By introducing the UUF module, we achieve the perfect fusion of shallow and deep information, making the information retained by the network in the feature extraction process more comprehensive. We have improved the Focal Loss to tackle the imbalance of pixel-level distribution. The results show that the SEG-LUS model exhibits significant performance improvement, with mPA, mDice, mIOU, and mASD reaching 85.05%, 82.60%, 74.92%, and 0.31, respectively. Compared with seven state-of-the-art semantic segmentation methods, the mPA improves by 5.32%. SEG-LUS is positioned to serve as a crucial reference for research in computer-aided modeling using liver US images, thereby advancing the field of US medicine research.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

Retinal vessel segmentation based on multi-scale feature and style transfer

Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g., they cannot obtain satisfactory results when dealing with cross-domain data or segmenting small blood vessels). To alleviate these problems and avoid overly complex models, we propose a novel network based on a multi-scale feature and style transfer (MSFST-NET) for retinal vessel segmentation. Specifically, we first construct a lightweight segmentation module named MSF-Net, which introduces the selective kernel (SK) module to increase the multi-scale feature extraction ability of the model to achieve improved small blood vessel segmentation. Then, to alleviate the problem of model performance degradation when segmenting cross-domain datasets, we propose a style transfer module and a pseudo-label learning strategy. The style transfer module is used to reduce the style difference between the source domain image and the target domain image to improve the segmentation performance for the target domain image. The pseudo-label learning strategy is designed to be combined with the style transfer module to further boost the generalization ability of the model. Moreover, we trained and tested our proposed MSFST-NET in experiments on the DRIVE and CHASE_DB1 datasets. The experimental results demonstrate that MSFST-NET can effectively improve the generalization ability of the model on cross-domain datasets and achieve improved retinal vessel segmentation results than other state-of-the-art 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.

SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision

Highlights

• Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets.

BACKGROUND

Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming.

OBJECTIVE

This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors.

METHOD

Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness.

RESULTS

The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively.

CONCLUSION

The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.

Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening

Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.

TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method

Retinal vessel segmentation plays a vital role in the medical field, facilitating the identification of numerous chronic conditions based on retinal vessel images. These conditions include diabetic retinopathy, hypertensive retinopathy, glaucoma, and others. Although the U-Net model has shown promising results in retinal vessel segmentation, it tends to struggle with fine branching and dense vessel segmentation. To further enhance the precision of retinal vessel segmentation, we propose a novel approach called transformer dilated convolution attention U-Net (TDCAU-Net), which builds upon the U-Net architecture with improved Transformer-based dilated convolution attention mechanisms. The proposed model retains the three-layer architecture of the U-Net network. The Transformer component enables the learning of contextual information for each pixel in the image, while the dilated convolution attention prevents information loss. The algorithm efficiently addresses several challenges to optimize blood vessel detection. The process starts with five-step preprocessing of the images, followed by chunking them into segments. Subsequently, the retinal images are fed into the modified U-Net network introduced in this paper for segmentation. The study employs eye fundus images from the DRIVE and CHASEDB1 databases for both training and testing purposes. Evaluation metrics are utilized to compare the algorithm's results with state-of-the-art methods. The experimental analysis on both databases demonstrates that the algorithm achieves high values of sensitivity, specificity, accuracy, and AUC. Specifically, for the first database, the achieved values are 0.8187, 0.9756, 0.9556, and 0.9795, respectively. For the second database, the corresponding values are 0.8243, 0.9836, 0.9738, and 0.9878, respectively. These results demonstrate that the proposed approach outperforms state-of-the-art methods, achieving higher performance on both datasets. The TDCAU-Net model presented in this study exhibits substantial capabilities in accurately segmenting fine branching and dense vessels. The segmentation performance of the network surpasses that of the U-Net algorithm and several mainstream methods.

Detection of ASD Children through Deep-Learning Application of fMRI

Autism spectrum disorder (ASD) necessitates prompt diagnostic scrutiny to enable immediate, targeted interventions. This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics-accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32%-and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model's hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention.

Addressing the programming challenges of practical interferometric mesh based optical processors

We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for programmable optical processors. We thoroughly analyze the benefits and drawbacks of previously known meshes and compare our newly proposed mesh with these prior architectures, highlighting its unique features and advantages. The proposed mesh, referred to as Bokun mesh, is an architecture that merges the attributes of the prior topologies Diamond and Clements. Similar to Diamond, Bokun provides diagonal paths passing through every individual MZI enabling direct phase monitoring. However, unlike Diamond and similar to Clements, Bokun maintains a minimum optical depth leading to better scalability. Providing the monitoring option, Bokun's programming is faster improving the total energy efficiency of the processor. The performance of Bokun mesh enabled by an optimal optical depth is also more resilient to the loss and fabrication imperfections compared to architectures with longer depth such as Reck and Diamond. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz.

TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation

Cerebrovascular segmentation in digital subtraction angiography (DSA) images is the gold standard for clinical diagnosis. However, owing to the complexity of cerebrovascular, automatic cerebrovascular segmentation in DSA images is a challenging task. In this paper, we propose a CNN-based Two-branch Boundary Enhancement Network (TBENet) for automatic segmentation of cerebrovascular in DSA images. The TBENet is inspired by U-Net and designed as an encoder-decoder architecture. We propose an additional boundary branch to segment the boundary of cerebrovascular and a Main and Boundary branches Fusion Module (MBFM) to integrate the boundary branch outcome with the main branch outcome to achieve better segmentation performance. The TBENet was evaluated on HMCDSA (an in-house DSA cerebrovascular dataset), and reaches 0.9611, 0.7486, 0.7152, 0.9860 and 0.9556 in Accuracy, F1 score, Sensitivity, Specificity, and AUC, respectively. Meanwhile, we tested our TBENet on the public vessel segmentation benchmark DRIVE, and the results show that our TBENet can be extended to diverse vessel segmentation tasks.

Depression Recognition From EEG Signals Using an Adaptive Channel Fusion Method via Improved Focal Loss

Depression is a serious and common psychiatric disease characterized by emotional and cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression are low. Therefore, the accurate recognition of depression is important for its effective treatment. Electroencephalogram (EEG) signals, which can objectively reflect the inner states of human brains, are regarded as promising physiological tools that can enable effective and efficient clinical depression diagnosis and recognition. However, one of the challenges regarding EEG-based depression recognition involves sufficiently optimizing the spatial information derived from the multichannel space of EEG signals. Consequently, we propose an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address this challenge. In this method, we propose two improved FL functions that can enhance the separability of hard examples by upweighting their losses as optimization objectives and can optimize the channel weights by a proposed adaptive channel fusion framework. The experimental results obtained on two EEG datasets show that the developed channel fusion method can achieve improved classification performance. The learned channel weights include the individual characteristics of each EEG epoch, which can effectively optimize the spatial information of each EEG epoch via the channel fusion method. In addition, the proposed method performs better than the state-of-the-art channel fusion methods.

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features

Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks

Retinal fundus images are non-invasively acquired and faced with low contrast, noise, and uneven illumination. The low-contrast problem makes objects in the retinal fundus image indistinguishable and the segmentation of blood vessels very challenging. Retinal blood vessels are significant because of their diagnostic importance in ophthalmologic diseases. This paper proposes improved retinal fundus images for optimal segmentation of blood vessels using convolutional neural networks (CNNs). This study explores some robust contrast enhancement tools on the RGB and the green channel of the retinal fundus images. The improved images undergo quality evaluation using mean square error (MSE), peak signal to noise ratio (PSNR), Similar Structure Index Matrix (SSIM), histogram, correlation, and intersection distance measures for histogram comparison before segmentation in the CNN-based model. The simulation results analysis reveals that the improved RGB quality outperforms the improved green channel. This revelation implies that the choice of RGB to the green channel for contrast enhancement is adequate and effectively improves the quality of the fundus images. This improved contrast will, in turn, boost the predictive accuracy of the CNN-based model during the segmentation process. The evaluation of the proposed method on the DRIVE dataset achieves an accuracy of 94.47, sensitivity of 70.92, specificity of 98.20, and AUC (ROC) of 97.56.

An optimized segmentation convolutional neural network with dynamic energy loss function for 3D reconstruction of lumbar spine MR images

3D reconstruction for lumbar spine based on segmentation of Magnetic Resonance (MR) images is meaningful for diagnosis of degenerative lumbar spine diseases. However, spine MR images with unbalanced pixel distribution often cause the segmentation performance of Convolutional Neural Network (CNN) reduced. Designing a composite loss function for CNN is an effective way to enhance the segmentation capacity, yet composition loss values with fixed weight may still cause underfitting in CNN training. In this study, we designed a composite loss function with a dynamic weight, called Dynamic Energy Loss, for spine MR images segmentation. In our loss function, the weight percentage of different loss values could be dynamically adjusted during training, thus CNN could fast converge in earlier training stage and focus on detail learning in the later stage. Two datasets were used in control experiments, and the U-net CNN model with our proposed loss function achieved superior performance with Dice similarity coefficient values of 0.9484 and 0.8284 respectively, which were also verified by the Pearson correlation, Bland-Altman, and intra-class correlation coefficient analysis. Furthermore, to improve the 3D reconstruction based on the segmentation results, we proposed a filling algorithm to generate contextually related slices by computing the pixel-level difference between adjacent slices of segmented images, which could enhance the structural information of tissues between slices, and improve the performance of 3D lumbar spine model rendering. Our methods could help radiologists to build a 3D lumbar spine graphical model accurately for diagnosis while reducing burden of manual image reading.

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search

Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but important for disentangling complex vascular network and tracking vessel morphology. In this paper, we present a novel directed graph search-based multi-attentive neural network approach to automatically segment the vascular network and separate intersections and bifurcations from color fundus images. Our approach uses multi-dimensional attention to adaptively integrate local features and their global dependencies while learning to focus on target structures at different scales to generate binary vascular maps. A directed graphical representation of the vascular network is constructed to represent the topology and spatial connectivity of the vascular structures. Using local geometric information including color difference, diameter, and angle, the complex vascular tree is decomposed into multiple sub-trees to finally classify and label vascular feature points. The proposed method has been tested on the DRIVE dataset and the IOSTAR dataset containing 40 images and 30 images, respectively, with 0.863 and 0.764 F¹-score of detection points and average accuracy of 0.914 and 0.854 for classification points. These results demonstrate the superiority of our proposed method outperforming state-of-the-art methods in feature point detection and classification.

Expectation-maximization algorithm leads to domain adaptation for a perineural invasion and nerve extraction task in whole slide digital pathology images

In addition to lymphatic and vascular channels, tumor cells can also spread via nerves, i.e., perineural invasion (PNI). PNI serves as an independent prognostic indicator in many malignancies. As a result, identifying and determining the extent of PNI is an important yet extremely tedious task in surgical pathology. In this work, we present a computational approach to extract nerves and PNI from whole slide histopathology images. We make manual annotations on selected prostate cancer slides once but then apply the trained model for nerve segmentation to both prostate cancer slides and head and neck cancer slides. For the purpose of multi-domain learning/prediction and investigation on the generalization capability of deep neural network, an expectation-maximization (EM)-based domain adaptation approach is proposed to improve the segmentation performance, in particular for the head and neck cancer slides. Experiments are conducted to demonstrate the segmentation performances. The average Dice coefficient for prostate cancer slides is 0.82 and 0.79 for head and neck cancer slides. Comparisons are then made for segmentations with and without the proposed EM-based domain adaptation on prostate cancer and head and neck cancer whole slide histopathology images from The Cancer Genome Atlas (TCGA) database and significant improvements are observed.

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning

BACKGROUND AND OBJECTIVES

Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some promising treatments have been proposed that effectively slow down its development, their effectiveness significantly diminishes in the advanced stages. This emphasizes the importance of large-scale screening programs for early detection. Nevertheless, implementing such programs for a disease like AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. For the characterization of the disease, clinicians have to identify and localize certain retinal lesions. All this motivates the development of automatic diagnostic methods. In this sense, several works have achieved highly positive results for AMD detection using convolutional neural networks (CNNs). However, none of them incorporates explainability mechanisms linking the diagnosis to its related lesions to help clinicians to better understand the decisions of the models. This is specially relevant, since the absence of such mechanisms limits the application of automatic methods in the clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions.

METHODS

In our proposal, a CNN with a custom architectural setting is trained end-to-end for the joint identification of AMD and its associated retinal lesions. With the proposed setting, the lesion identification is directly derived from independent lesion activation maps; then, the diagnosis is obtained from the identified lesions. The training is performed end-to-end using image-level labels. Thus, lesion-specific activation maps are learned in a weakly-supervised manner. The provided lesion information is of high clinical interest, as it allows clinicians to assess the developmental stage of the disease. Additionally, the proposed approach allows to explain the diagnosis obtained by the models directly from the identified lesions and their corresponding activation maps. The training data necessary for the approach can be obtained without much extra work on the part of clinicians, since the lesion information is habitually present in medical records. This is an important advantage over other methods, including fully-supervised lesion segmentation methods, which require pixel-level labels whose acquisition is arduous.

RESULTS

The experiments conducted in 4 different datasets demonstrate that the proposed approach is able to identify AMD and its associated lesions with satisfactory performance. Moreover, the evaluation of the lesion activation maps shows that the models trained using the proposed approach are able to identify the pathological areas within the image and, in most cases, to correctly determine to which lesion they correspond.

CONCLUSIONS

The proposed approach provides meaningful information-lesion identification and lesion activation maps-that conveniently explains and complements the diagnosis, and is of particular interest to clinicians for the diagnostic process. Moreover, the data needed to train the networks using the proposed approach is commonly easy to obtain, what represents an important advantage in fields with particularly scarce data, such as medical imaging.

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.

Attention aware fully convolutional deep learning model for retinal blood vessel segmentation

Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases.

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.

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4 s, which has been identified on boundary condition of Pdf function.

Recent trends and advances in fundus image analysis: A review

Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

Modified capsule network for diabetic retinopathy detection and classification using fundus images

Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy.

A Detailed Systematic Review on Retinal Image Segmentation Methods

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association

With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.

Analysis Model of Image Colour Data Elements Based on Deep Neural Network

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability.

Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?

Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.

Asymmetry between right and left optical coherence tomography images identified using convolutional neural networks

In a previous study, we identified biocular asymmetries in fundus photographs, and macula was discriminative area to distinguish left and right fundus images with > 99.9% accuracy. The purposes of this study were to investigate whether optical coherence tomography (OCT) images of the left and right eyes could be discriminated by convolutional neural networks (CNNs) and to support the previous result. We used a total of 129,546 OCT images. CNNs identified right and left horizontal images with high accuracy (99.50%). Even after flipping the left images, all of the CNNs were capable of discriminating them (DenseNet121: 90.33%, ResNet50: 88.20%, VGG19: 92.68%). The classification accuracy results were similar for the right and left flipped images (90.24% vs. 90.33%, respectively; p = 0.756). The CNNs also differentiated right and left vertical images (86.57%). In all cases, the discriminatory ability of the CNNs yielded a significant p value (< 0.001). However, the CNNs could not well-discriminate right horizontal images (50.82%, p = 0.548). There was a significant difference in identification accuracy between right and left horizontal and vertical OCT images and between flipped and non-flipped images. As this could result in bias in machine learning, care should be taken when flipping images.

Deep Feature Extraction for Cymbidium Species Classification Using Global–Local CNN

Cymbidium is the most famous and widely distributed type of plant in the Orchidaceae family. It has extremely high ornamental and economic value. With the continuous development of the Cymbidium industry in recent years, it has become increasingly difficult to classify, identify, develop, and utilize orchids. In this study, a classification model GL-CNN based on a convolutional neural network was proposed to solve the problem of Cymbidium classification. First, the image set was expanded by four methods (mirror rotation, salt-and-pepper noise, image sharpening, and random angle flip), and then a cascade fusion strategy was used to fit the multiscale features obtained from the two branches. Comparing the performance of GL-CNN with other four classic models (AlexNet, ResNet50, GoogleNet, and VGG16), the results showed that GL-CNN achieves the highest classification prediction accuracy with a value of 94.13%. This model can effectively detect different species of Cymbidium and provide a reference for the identification of Cymbidium germplasm resources.

Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis

Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis.

DilUnet: A U-net based architecture for blood vessels segmentation

BACKGROUND AND OBJECTIVE

Retinal image segmentation can help clinicians detect pathological disorders by studying changes in retinal blood vessels. This early detection can help prevent blindness and many other vision impairments. So far, several supervised and unsupervised methods have been proposed for the task of automatic blood vessel segmentation. However, the sensitivity and the robustness of these methods can be improved by correctly classifying more vessel pixels.

METHOD

We proposed an automatic, retinal blood vessel segmentation method based on the U-net architecture. This end-to-end framework utilizes preprocessing and a data augmentation pipeline for training. The architecture utilizes multiscale input and multioutput modules with improved skip connections and the correct use of dilated convolutions for effective feature extraction. In multiscale input, the input image is scaled down and concatenated with the output of convolutional blocks at different points in the encoder path to ensure the feature transfer of the original image. The multioutput module obtains upsampled outputs from each decoder block that are combined to obtain the final output. Skip paths connect each encoder block with the corresponding decoder block, and the whole architecture utilizes different dilation rates to improve the overall feature extraction.

RESULTS

The proposed method achieved an accuracy: of 0.9680, 0.9694, and 0.9701; sensitivity of 0.8837, 0.8263, and 0.8713; and Intersection Over Union (IOU) of 0.8698, 0.7951, and 0.8184 with three publicly available datasets: DRIVE, STARE, and CHASE, respectively. An ablation study is performed to show the contribution of each proposed module and technique.

CONCLUSION

The evaluation metrics revealed that the performance of the proposed method is higher than that of the original U-net and other U-net-based architectures, as well as many other state-of-the-art segmentation techniques, and that the proposed method is robust to noise.

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.

An FA-SegNet Image Segmentation Model Based on Fuzzy Attention and Its Application in Cardiac MRI Segmentation

Aiming at the medical images segmentation with low-recognition and high background noise, a deep convolution neural network image segmentation model based on fuzzy attention mechanism is proposed, which is called FA-SegNet. It takes SegNet as the basic framework. In the down-sampling module for image feature extraction, a fuzzy channel-attention module is added to strengthen the discrimination of different target regions. In the up-sampling module for image size restoration and multi-scale feature fusion, a fuzzy spatial-attention module is added to reduce the loss of image details and expand the receptive field. In this paper, fuzzy cognition is introduced into the feature fusion of CNNs. Based on the attention mechanism, fuzzy membership is used to re-calibrate the importance of the pixel value in local regions. It can strengthen the distinguishing ability of image features, and the fusion ability of the contextual information, which improves the segmentation accuracy of the target regions. Taking MRI segmentation as an experimental example, multiple targets such as the left ventricles, right ventricles, and left ventricular myocardium are selected as the segmentation targets. The pixels accuracy is 92.47%, the mean intersection to union is 86.18%, and the Dice coefficient is 92.44%, which are improved compared with other methods. It verifies the accuracy and applicability of the proposed method for the medical images segmentation, especially the targets with low-recognition and serious occlusion.

Retinal Vessel Automatic Segmentation Using SegNet

Extracting retinal vessels accurately is very important for diagnosing some diseases such as diabetes retinopathy, hypertension, and cardiovascular. Clinically, experienced ophthalmologists diagnose these diseases through segmenting retinal vessels manually and analysing its structural feature, such as tortuosity and diameter. However, manual segmentation of retinal vessels is a time-consuming and laborious task with strong subjectivity. The automatic segmentation technology of retinal vessels can not only reduce the burden of ophthalmologists but also effectively solve the problem that is a lack of experienced ophthalmologists in remote areas. Therefore, the automatic segmentation technology of retinal vessels is of great significance for clinical auxiliary diagnosis and treatment of ophthalmic diseases. A method using SegNet is proposed in this paper to improve the accuracy of the retinal vessel segmentation. The performance of the retinal vessel segmentation model with SegNet is evaluated on the three public datasets (DRIVE, STARE, and HRF) and achieved accuracy of 0.9518, 0.9683, and 0.9653, sensitivity of 0.7580, 0.7747, and 0.7070, specificity of 0.9804, 0.9910, and 0.9885, F 1 score of 0.7992, 0.8369, and 0.7918, MCC of 0.7749, 0.8227, and 0.7643, and AUC of 0.9750, 0.9893, and 0.9740, respectively. The experimental results showed that the method proposed in this research presented better results than many classical methods studied and may be expected to have clinical application prospects.