Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data.

BACKGROUND AND OBJECTIVE: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. METHODS: We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over-sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. RESULTS: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. CONCLUSIONS: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data.

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting-state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer-Encoder model, which utilized functional connectivity extracted from large-scale multisite rs-fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end-to-end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre-training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large-scale multisite dataset and identified brain regions affected by MDD using the Grad-CAM method. After conducting five-fold cross-validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.

Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data.

BACKGROUND: Major depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression. METHOD: We propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD. RESULT: We achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified. LIMITATION: The pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance. CONCLUSION: The experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

Semi-supervised OCT lesion segmentation via transformation-consistent with uncertainty and self-deep supervision.

Optical coherence tomography (OCT) is a non-invasive, high-resolution ocular imaging technique with important implications for the diagnosis and management of retinal diseases. Automatic segmentation of lesions in OCT images is critical for assessing disease progression and treatment outcomes. However, existing methods for lesion segmentation require numerous pixel-wise annotations, which are difficult and time-consuming to obtain. To address this challenge, we propose a novel framework for semi-supervised OCT lesion segmentation, termed transformation-consistent with uncertainty and self-deep supervision (TCUS). To address the issue of lesion area blurring in OCT images and unreliable predictions from the teacher network for unlabeled images, an uncertainty-guided transformation-consistent strategy is proposed. Transformation-consistent is used to enhance the unsupervised regularization effect. The student network gradually learns from meaningful and reliable targets by utilizing the uncertainty information from the teacher network, to alleviate the performance degradation caused by potential errors in the teacher network's prediction results. Additionally, self-deep supervision is used to acquire multi-scale information from labeled and unlabeled OCT images, enabling accurate segmentation of lesions of various sizes and shapes. Self-deep supervision significantly improves the accuracy of lesion segmentation in terms of the Dice coefficient. Experimental results on two OCT datasets demonstrate that the proposed TCUS outperforms state-of-the-art semi-supervised segmentation methods.

FDCT: Fusion-Guided Dual-View Consistency Training for semi-supervised tissue segmentation on MRI.

Accurate tissue segmentation on MRI is important for physicians to make diagnosis and treatment for patients. However, most of the models are only designed for single-task tissue segmentation, and tend to lack generality to other MRI tissue segmentation tasks. Not only that, the acquisition of labels is time-consuming and laborious, which remains a challenge to be solved. In this study, we propose the universal Fusion-Guided Dual-View Consistency Training(FDCT) for semi-supervised tissue segmentation on MRI. It can obtain accurate and robust tissue segmentation for multiple tasks, and alleviates the problem of insufficient labeled data. Especially, for building bidirectional consistency, we feed dual-view images into a single-encoder dual-decoder structure to obtain view-level predictions, then put them into a fusion module to generate image-level pseudo-label. Moreover, to improve boundary segmentation quality, we propose the Soft-label Boundary Optimization Module(SBOM). We have conducted extensive experiments on three MRI datasets to evaluate the effectiveness of our method. Experimental results demonstrate that our method outperforms the state-of-the-art semi-supervised medical image segmentation methods.

Altered Effective Connectivity Among the Cerebellum and Cerebrum in Patients with Major Depressive Disorder Using Multisite Resting-State fMRI.

Major depressive disorder (MDD) is a serious and widespread psychiatric disorder. Previous studies mainly focused on cerebrum functional connectivity, and the sample size was relatively small. However, functional connectivity is undirected. And, there is increasing evidence that the cerebellum is also involved in emotion and cognitive processing and makes outstanding contributions to the symptomology and pathology of depression. Therefore, we used a large sample size of resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate the altered effective connectivity (EC) among the cerebellum and other cerebral cortex in patients with MDD. Here, from the perspective of data-driven analysis, we used two different atlases to divide the whole brain into different regions and analyzed the alterations of EC and EC networks in the MDD group compared with healthy controls group (HCs). The results showed that compared with HCs, there were significantly altered EC in the cerebellum-neocortex and cerebellum-basal ganglia circuits in MDD patients, which implied that the cerebellum may be a potential biomarker of depressive disorders. And, the alterations of EC brain networks in MDD patients may provide new insights into the pathophysiological mechanisms of depression.

The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data.

BACKGROUND: The current diagnosis of major depressive disorder (MDD) is mainly based on the patient's self-report and clinical symptoms. Machine learning methods are used to identify MDD using resting-state functional magnetic resonance imaging (rs-fMRI) data. However, due to large site differences in multisite rs-fMRI data and the difficulty of sample collection, most of the current machine learning studies use small sample sizes of rs-fMRI datasets to detect the alterations of functional connectivity (FC) or network attribute (NA), which may affect the reliability of the experimental results. METHODS: Multisite rs-fMRI data were used to increase the size of the sample, and then we extracted the functional connectivity (FC) and network attribute (NA) features from 1611 rs-fMRI data (832 patients with MDD (MDDs) and 779 healthy controls (HCs)). ComBat algorithm was used to harmonize the data variances caused by the multisite effect, and multivariate linear regression was used to remove age and sex covariates. Two-sample t-test and wrapper-based feature selection methods (support vector machine recursive feature elimination with cross-validation (SVM-RFECV) and LightGBM's "feature_importances_" function) were used to select important features. The Shapley additive explanations (SHAP) method was used to assign the contribution of features to the best classification effect model. RESULTS: The best result was obtained from the LinearSVM model trained with the 136 important features selected by SVMRFE-CV. In the nested five-fold cross-validation (consisting of an outer and an inner loop of five-fold cross-validation) of 1611 data, the model achieved the accuracy, sensitivity, and specificity of 68.90 %, 71.75 %, and 65.84 %, respectively. The 136 important features were tested in a small dataset and obtained excellent classification results after balancing the ratio between patients with depression and HCs. CONCLUSIONS: The combined use of FC and NA features is effective for classifying MDDs and HCs. The important FC and NA features extracted from the large sample dataset have some generalization performance and may be used as a reference for the altered brain functional connectivity networks in MDD.

A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis.

Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.

Dual-consistency semi-supervision combined with self-supervision for vessel segmentation in retinal OCTA images.

Optical coherence tomography angiography(OCTA) is an advanced noninvasive vascular imaging technique that has important implications in many vision-related diseases. The automatic segmentation of retinal vessels in OCTA is understudied, and the existing segmentation methods require large-scale pixel-level annotated images. However, manually annotating labels is time-consuming and labor-intensive. Therefore, we propose a dual-consistency semi-supervised segmentation network incorporating multi-scale self-supervised puzzle subtasks(DCSS-Net) to tackle the challenge of limited annotations. First, we adopt a novel self-supervised task in assisting semi-supervised networks in training to learn better feature representations. Second, we propose a dual-consistency regularization strategy that imposed data-based and feature-based perturbation to effectively utilize a large number of unlabeled data, alleviate the overfitting of the model, and generate more accurate segmentation predictions. Experimental results on two OCTA retina datasets validate the effectiveness of our DCSS-Net. With very little labeled data, the performance of our method is comparable with fully supervised methods trained on the entire labeled dataset.

Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia.

Objectives: The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods: The FAZ boundaries on the full-thickness retina of 6 × 6 mm en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results: This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions: The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.

Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning.

Glaucoma diagnosis often suffers from two types of data imbalances: 1) class imbalance, i.e., the non-glaucoma majority cases occupy most of the data; 2) rare cases, i.e., few cases present the uncommon retinopathy e.g., bayoneting or physiologic cupping. This dual-imbalances make glaucoma diagnosis model easy to be dominated by the majority cases but cannot correctly classify the minority and/or rare ones. In this paper, we propose an adaptive re-balancing strategy in the feature space, Self-Ensemble Dual-Curriculum learning (SEDC), to improve the glaucoma diagnosis on imbalanced data by augmenting feature distribution with feature distilling and feature re-weighting. Firstly, the self-ensembling (SEL) is developed to reinforce the discriminative ability of feature representations for the minority class and rare cases by distilling the features learned from the abundant majority cases. Secondly, the dual-curriculum (DCL) is designed to adaptively re-weight the imbalanced data in the feature space to learn a balanced decision function for accurate glaucoma diagnosis. Benefiting from feature distilling and re-weighting, the proposed SEDC fairly represents fundus images, regardless of the majority or rare cases, by augmenting the feature distribution to obtains the optimal decision boundary for accurate glaucoma diagnosis on the imbalanced dataset. Experimental results on three challenging glaucoma datasets show that our SEDC successfully delivers accurate glaucoma diagnosis by the adaptive re-balancing strategy, with the average mean value of Accuracy 0.9712, Sensitivity 0.9520, Specificity 0.9816, AUC 0.9928, F2-score 0.9547. Ablation and comparison studies demonstrate that our method outperforms state-of-the-art methods and traditional re-balancing strategies. The experiment also shows that the adaptive re-balancing strategy proposed in our method provides a more effective training approach with optimal convergence performance. It endows our SEDC a great advantage to handle the disease diagnosis on imbalanced data distribution.

Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study.

The altered functional connectivity (FC) in amblyopia has been investigated by many studies, but the specific causality of brain connectivity needs to be explored further to understand the brain activity of amblyopia. We investigated whether the effective connectivity (EC) of children and young adults with amblyopia was altered. The subjects included 16 children and young adults with left eye amblyopia and 17 healthy controls (HCs). The abnormalities between the left/right primary visual cortex (PVC) and the other brain regions were investigated in a voxel-wise manner using the Granger causality analysis (GCA). According to the EC results in the HCs and the distribution of visual pathways, 12 regions of interest (ROIs) were selected to construct an EC network. The alteration of the EC network of the children and young adults with amblyopia was analyzed. In the voxel-wise manner analysis, amblyopia showed significantly decreased EC between the left/right of the PVC and the left middle frontal gyrus/left inferior frontal gyrus compared with the HCs. In the EC network analysis, compared with the HCs, amblyopia showed significantly decreased EC from the left calcarine fissure, posterior cingulate gyrus, left lingual gyrus, right lingual gyrus, and right fusiform gyrus to the right calcarine fissure. Amblyopia also showed significantly decreased EC from the right inferior frontal gyrus and right lingual gyrus to the left superior temporal gyrus compared with the HCs in the EC network analysis. The results may indicate that amblyopia altered the visual feedforward and feedback pathway, and amblyopia may have a greater relevance with the feedback pathway than the feedforward pathway. Amblyopia may also correlate with the feedforward of the third visual pathway.

The delineation of largely deformed brain midline using regression-based line detection network.

PURPOSE: The human brain has two cerebral hemispheres that are roughly symmetric and separated by a midline, which is nearly a straight line shown in axial computed tomography (CT) images in healthy subjects. However, brain diseases such as hematoma and tumors often cause midline shift, where the degree of shift can be regarded as a quantitative indication in clinical practice. To facilitate clinical evaluation, we need computer-aided methods to automate this quantification. Nevertheless, most existing studies focused on the landmark- or symmetry-based methods that provide only the existence of shift or its maximum distance, which could be easily affected by anatomical variability and large brain deformations. Intuitive results such as midline delineation or measurement are lacking. In this study, we focus on developing an automated and robust method based on the fully convolutional neural network for the delineation of midline in largely deformed brains. METHODS: We propose a novel regression-based line detection network (RLDN) for the robust midline delineation, especially in largely deformed brains. Specifically, to improve the robustness of delineation in largely deformed brains, we regard the delineation of the midline as the skeleton extraction task and then use the multiscale bidirectional integration module to acquire more representative features. Based on the skeleton extraction, we incorporate the regression task into it to delineate more accurate and continuous midline, especially in largely deformed brains. Our study utilized the public CQ 500 dataset (128 subjects) for training with hold-out validation on 61 subjects from a private cohort accrued from a local hospital. RESULTS: The mean line distance error and F1-score were 1.17 ± 0.72 mm with 0.78 on CQ 500 test set, and 4.15 ± 3.97 mm with 0.61 on the private dataset. Besides, significant differences (P < 0.05) were observed between our method and other comparative ones on these two datasets. CONCLUSIONS: This work provides a novel solution to acquire robust delineation of the midline, especially in largely deformed brains, and achieves state-of-the-art performance on the public and our private dataset, which makes it possible for automated diagnosis of relevant brain diseases in the future.

Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image with a convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0.0563 and a higher correlation of around 0.726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0.905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

Retinal vessel segmentation using dense U-net with multiscale inputs.

A color fundus image is an image of the inner wall of the eyeball taken with a fundus camera. Doctors can observe retinal vessel changes in the image, and these changes can be used to diagnose many serious diseases such as atherosclerosis, glaucoma, and age-related macular degeneration. Automated segmentation of retinal vessels can facilitate more efficient diagnosis of these diseases. We propose an improved U-net architecture to segment retinal vessels. Multiscale input layer and dense block are introduced into the conventional U-net, so that the network can make use of richer spatial context information. The proposed method is evaluated on the public dataset DRIVE, achieving 0.8199 in sensitivity and 0.9561 in accuracy. Especially for thin blood vessels, which are difficult to detect because of their low contrast with the background pixels, the segmentation results have been improved.

Altered Spontaneous Brain Activity of Children with Unilateral Amblyopia: A Resting State fMRI Study.

Objective: This study is aimed at investigating differences in local brain activity and functional connectivity (FC) between children with unilateral amblyopia and healthy controls (HCs) by using resting state functional magnetic resonance imaging (rs-fMRI). Methods: Local activity and FC analysis methods were used to explore the altered spontaneous brain activity of children with unilateral amblyopia. Local brain function analysis methods included the amplitude of low-frequency fluctuation (ALFF). FC analysis methods consisted of the FC between the primary visual cortex (PVC-FC) and other brain regions and the FC network between regions of interest (ROIs-FC) selected by independent component analysis. Results: The ALFF in the bilateral frontal, temporal, and occipital lobes in the amblyopia group was lower than that in the HCs. The weakened PVC-FC was mainly concentrated in the frontal lobe and the angular gyrus. The ROIs-FC between the default mode network, salience network, and primary visual cortex network (PVCN) were significantly reduced, whereas the ROIs-FC between the PVCN and the high-level visual cortex network were significantly increased in amblyopia. Conclusions: Unilateral amblyopia may reduce local brain activity and FC in the dorsal and ventral visual pathways and affect the top-down attentional control. Amblyopia may also alter FC between brain functional networks. These findings may help understand the pathological mechanisms of children with amblyopia.

Combination of Enhanced Depth Imaging Optical Coherence Tomography and Fundus Images for Glaucoma Screening.

Glaucoma is an eye disease that damages the optic nerve and can lead to irreversible loss of peripheral vision gradually and even blindness without treatment. Thus, diagnosing glaucoma in the early stage is essential for treatment. In this paper, an automatic method for early glaucoma screening is proposed. The proposed method combines structural parameters and textural features extracted from enhanced depth imaging optical coherence tomography (EDI-OCT) images and fundus images. The method first segments anterior the lamina cribrosa surface (ALCS) based on region-aware strategy and residual U-Net and then extracts structural features of the lamina cribrosa, such as lamina cribrosa depth and deformation of lamina cribrosa. In fundus images, scanning lines based on disc center and brightness reduction are used for optic disc segmentation and brightness compensation is utilized for segmenting the optic cup. Afterward, the cup-to-disc ratio (CDR) and textural features are extracted from fundus images. Hybrid features are used for training and classification to screen glaucoma by gcForest in the early stage. The proposed method has given exceptional results with 96.88% accuracy and 91.67% sensitivity.

Region-segmentation strategy for Bruch's membrane opening detection in spectral domain optical coherence tomography images.

Bruch's membrane opening (BMO) is an important biomarker in the progression of glaucoma. Bruch's membrane opening minimum rim width (BMO-MRW), cup-to-disc ratio in spectral domain optical coherence tomography (SD-OCT) and lamina cribrosa depth based on BMO are important measurable parameters for glaucoma diagnosis. The accuracy of measuring these parameters is significantly affected by BMO detection. In this paper, we propose a method for automatically detecting BMO in SD-OCT volumes accurately to reduce the impact of the border tissue and vessel shadows. The method includes three stages: a coarse detection stage composed by retinal pigment epithelium layer segmentation, optic disc segmentation, and multi-modal registration; a fixed detection stage based on the U-net in which BMO detection is transformed into a region segmentation problem and an area bias component is proposed in the loss function; and a post-processing stage based on the consistency of results to remove outliers. Experimental results show that the proposed method outperforms previous methods and achieves a mean error of 42.38 µm.

Retinal vessel optical coherence tomography images for anemia screening.

Anemia is a disease that leads to low oxygen carrying capacity in the blood. Early detection of anemia is critical for the diagnosis and treatment of blood diseases. We find that retinal vessel optical coherence tomography (OCT) images of patients with anemia have abnormal performance because the internal material of the vessel absorbs light. In this study, an automatic anemia screening method based on retinal vessel OCT images is proposed. The method consists of seven steps, namely, denoising, region of interest (ROI) extraction, layer segmentation, vessel segmentation, feature extraction, feature dimensionality reduction, and classification. We propose gradient and threshold algorithm for ROI extraction and improve region growing algorithm based on adaptive seed point for vessel segmentation. We also conduct a statistical analysis of the correlation between hemoglobin concentration and intravascular brightness and vascular shadow in OCT images before feature extraction. Eighteen statistical features and 118 texture features are extracted for classification. This study is the first to use retinal vessel OCT images for anemia screening. Experimental results demonstrate the accuracy of the proposed method is 0.8358, which indicates that the method has clinical potential for anemia screening. Graphical abstract.

A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.

The automatic exudate segmentation in colour retinal fundus images is an important task in computer aided diagnosis and screening systems for diabetic retinopathy. In this paper, we present a location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images, which includes three stages: anatomic structure removal, exudate location and exudate segmentation. In anatomic structure removal stage, matched filters based main vessels segmentation method and a saliency based optic disk segmentation method are proposed. The main vessel and optic disk are then removed to eliminate the adverse affects that they bring to the second stage. In the location stage, we learn a random forest classifier to classify patches into two classes: exudate patches and exudate-free patches, in which the histograms of completed local binary patterns are extracted to describe the texture structures of the patches. Finally, the local variance, the size prior about the exudate regions and the local contrast prior are used to segment the exudate regions out from patches which are classified as exudate patches in the location stage. We evaluate our method both at exudate-level and image-level. For exudate-level evaluation, we test our method on e-ophtha EX dataset, which provides pixel level annotation from the specialists. The experimental results show that our method achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly. For image-level evaluation, we test our method on DiaRetDB1, and achieve competitive performance compared to the state of the art methods.