Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI

BACKGROUND AND OBJECTIVE

The diagnosis of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) mainly relies on objective cognitive assessment, clinical observation, and neuro-psychological evaluation, while only adopting clinical tools often limits the diagnosis accuracy.

METHODS

We proposed a multi-modal feature selection framework with higher-order correlated topological manifold (HCTMFS) to classify ESRDaMCI patients and identify the discriminative brain regions. It constructed brain structural and functional networks with diffuse kurtosis imaging (DKI) and functional magnetic resonance imaging (fMRI) data, and extracted node efficiency and clustering coefficient from the brain networks to construct multi-modal feature matrices. The topological relationship matrices were constructed to measure the lower-order topological correlation between features. Then the consensus matrices were learned to approximate the topological relationship matrices at different confidence levels and eliminate the noise influence of individual matrices.

RESULTS

The higher-order topological correlation between features was explored by the Laplacian matrix of the hypergraph, which was calculated through the consensus matrix. The new framework achieved an accuracy rate of 93.56 % for classifying ESRDaMCI patients, and outperformed the existing state-of-the-art methods in terms of sensitivity, specificity, and area under the curve.

CONCLUSIONS

This study contributes to effectively reflect the functional neural degradation of ESRDaMCI and provide a reference for the diagnosis of ESRDaMCI by selecting discriminative brain regions.

Assessment of Narrow Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer

Video capsule endoscopy (VCE) is increasingly used to decrease discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared to NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can presently only be achieved in post-processing. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to assess the simulated NBI images. Results show the good performance of the NBI image reproduction method with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.

Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer's disease classification

Deep learning (DL) algorithms based on brain MRI images have achieved great success in the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that of the most experienced clinical experts. As a novel feature fusion method, Transformer has achieved excellent performance in many computer vision tasks, which also greatly promotes the application of Transformer in medical images. However, when Transformer is used for 3D MRI image feature fusion, existing DL models treat the input local features equally, which is inconsistent with the fact that adjacent voxels have stronger semantic connections than spatially distant voxels. In addition, due to the relatively small size of the dataset for medical images, it is difficult to capture local lesion features in limited iterative training by treating all input features equally. This paper proposes a deep learning model Conv-Swinformer that focuses on extracting and integrating local fine-grained features. Conv-Swinformer consists of a CNN module and a Transformer encoder module. The CNN module summarizes the planar features of the MRI slices, and the Transformer module establishes semantic connections in 3D space for these planar features. By introducing the shift window attention mechanism in the Transformer encoder, the attention is focused on a small spatial area of the MRI image, which effectively reduces unnecessary background semantic information and enables the model to capture local features more accurately. In addition, the layer-by-layer enlarged attention window can further integrate local fine-grained features, thus enhancing the model's attention ability. Compared with DL algorithms that indiscriminately fuse local features of MRI images, Conv-Swinformer can fine-grained extract local lesion features, thus achieving better classification results.

Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression. We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.

Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment

Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships of brain regions and remove noise from the feature matrix. A multimodal classification framework is proposed to address this issue using hypergraph latent relation (HLR). A brain functional network with hypergraph structural information is constructed by fMRI data. The feature matrix is obtained through graph theory (GT). The cerebral blood flow (CBF) from ASL is selected as the second modal feature matrix. Then, the adaptive similarity matrix is constructed by learning the latent relation between feature matrices. Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). The experimental results show that the best classification accuracy (ACC) reaches 88.67%, at least 2.84% better than the state-of-the-art methods. The proposed framework preserves more valuable information between brain regions and reduces noise among feature matrixes. It provides an essential reference value for ESRDaMCI recognition.

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.

Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

Classification of neuroimaging data in Alzheimer's disease using particle swarm optimization: A systematic review

AIM

Particle swarm optimization (PSO) is an algorithm that involves the optimization of Non-linear and Multidimensional problems to reach the best solutions with minimal parameterization. This metaheuristic model has frequently been used in the Pathological domain. This optimization model has been used in diverse forms while predicting Alzheimer's disease. It is a robust algorithm that works on linear and multi-modal data while predicting Alzheimer's disease. PSO techniques have been in action for quite some time for detecting various diseases and this paper systematically reviews the papers on various kinds of PSO techniques.

METHODS

To perform the systematic review, PRISMA guidelines were followed and a Boolean search ("particle swarm optimization" OR "PSO") AND Neuroimaging AND (Alzheimer's disease prediction OR classification OR diagnosis) were performed. The query was run in 4-reputed databases: Google Scholar, Scopus, Science Direct, and Wiley publications.

RESULTS

For the final analysis, 10 papers were incorporated for qualitative and quantitative synthesis. PSO has shown a dominant character while handling the uni-modal as well as the multi-modal data while predicting the conversion from MCI to Alzheimer's. It can be seen from the table that almost all the 10 reviewed papers had MRI-driven data. The accuracy rate was accentuated while adding other modalities or Neurocognitive measures.

CONCLUSIONS

Through this algorithm, we are providing an opportunity to other researchers to compare this algorithm with other state-of-the-art algorithms, while seeing the classification accuracy, with the aim of early prediction and progression of MCI into Alzheimer's disease.

Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment

The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and L₁ norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.

Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters.

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment

Purpose

To characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.

Materials and methods

In total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).

Results

We observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.

Conclusion

Disruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease

Background

Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care.

Methods

We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO).

Results

The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype.

Conclusions

This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification

Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data

Multimodality neuroimages have been widely applied to diagnose mild cognitive impairment (MCI). However, the missing data problem is unavoidable. Most previously developed methods first train a generative adversarial network (GAN) to synthesize missing data and then train a classification network with the completed data. These methods independently train two networks with no information communication. Thus, the resulting GAN cannot focus on the crucial regions that are helpful for classification. To overcome this issue, we propose a hybrid deep learning method. First, a classification network is pretrained with paired MRI and PET images. Afterward, we use the pretrained classification network to guide a GAN by focusing on the features that are helpful for classification. Finally, we synthesize the missing PET images and use them with real MR images to fine-tune the classification model to make it better adapt to the synthesized images. We evaluate our proposed method on the ADNI dataset, and the results show that our method improves the accuracies obtained on the validation and testing sets by 3.84 and 5.82%, respectively. Moreover, our method increases the accuracies for the validation and testing sets by 7.7 and 9.09%, respectively, when we synthesize the missing PET images via our method. An ablation experiment shows that the last two stages are essential for our method. We also compare our method with other state-of-the-art methods, and our method achieves better classification performance.

GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease

The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.

Improved Wavelet Filter Bank Selection for Effective Feature Extraction in Alzheimer Classification

Background: Alzheimer’s disease (AD) is the primary reason for health problem. Motivation: Being degenerative and progressive with brain cells that can be intervened by health professionals in case of early recognition. Feature extraction is a technique employed for reduction of dimensionality. The features are generated for a image. The extraction of features has to be done accurately without any loss of information. Methods: In this work, a Cuckoo Search (CS) based Wavelet Filter Bank Selection algorithm for classification of Alzheimer’s has been proposed. The Ada Boost classifier, Random Forest (RF), and Classification and Regression Tree (CART) were used for the identification of the affected patient with Magnetic Resonance Imaging (MRI). Results: From results it can be found that proposed CS-based technique is used in classifying AD compared to conventional techniques.

Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification

Feature selection for multiple types of data has been widely applied in mild cognitive impairment (MCI) and Alzheimer's disease (AD) classification research. Combining multi-modal data for classification can better realize the complementarity of valuable information. In order to improve the classification performance of feature selection on multi-modal data, we propose a multi-modal feature selection algorithm using feature correlation and feature structure fusion (FC2FS). First, we construct feature correlation regularization by fusing a similarity matrix between multi-modal feature nodes. Then, based on manifold learning, we employ feature matrix fusion to construct feature structure regularization, and learn the local geometric structure of the feature nodes. Finally, the two regularizations are embedded in a multi-task learning model that introduces low-rank constraint, the multi-modal features are selected, and the final features are linearly fused and input into a support vector machine (SVM) for classification. Different controlled experiments were set to verify the validity of the proposed method, which was applied to MCI and AD classification. The accuracy of normal controls versus Alzheimer's disease, normal controls versus late mild cognitive impairment, normal controls versus early mild cognitive impairment, and early mild cognitive impairment versus late mild cognitive impairment achieve 91.85 ± 1.42%, 85.33 ± 2.22%, 78.29 ± 2.20%, and 77.67 ± 1.65%, respectively. This method makes up for the shortcomings of the traditional multi-modal feature selection based on subjects and fully considers the relationship between feature nodes and the local geometric structure of feature space. Our study not only enhances the interpretation of feature selection but also improves the classification performance, which has certain reference values for the identification of MCI and AD.

Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer's Disease

Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer’s disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment.

Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t-test were used to explain the potential causes of uncertain cases.

Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant (p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent (p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state (p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected (p < 0.05).

Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.

Prediction of neuro-degenerative disorders using sunflower optimisation algorithm and Kernel extreme learning machine: A case-study with Parkinson's and Alzheimer's disease

Parkinson's and Alzheimer's Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer's (AD) and Parkinson's (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.

Evaluation of the Image Feature Analysis Techniques by Image Strength Fluctuation for Alzheimer’s Disease

This article compares the effectiveness of both image strength fluctuation (ISF) and statistical parametric mapping (SPM), which is used for analyzing cerebral blood flow traced by single photon emission computed tomography (SPECT) in patients with Alzheimer’s disease (AD). This is a retrospective study that uses the built-in SPECT template of the SPM software to analyze the differences in cerebral blood flow (CBF) between two groups after using the ISF and SPM software to normalize and smooth the data. Using Z score analysis, the ISF method revealed unusual fluctuations in the range of image strength. The hot and cold areas were considered to be the difference between normal and abnormal images and the analysis of the gray intensities of cold and hot areas was used to show that cold and hot were in characteristic locations that correlate with brain functional areas. The results of the SPM analysis revealed significantly reduced blood flow in the bilateral inferior parietal lobule, the middle or superior gyrus of the left temporal lobe, the superior gyrus and the sub-lobar insula of the right temporal lobe. The results from SPM were similar to those obtained with the ISF and indicated the same regions (83.3%).

Brain MRI Intelligent Diagnostic Using an Improved Deep Convolutional Neural Network

The diagnosis of brain diseases based on magnetic resonance imaging (MRI) is a mainstream practice. In the course of practical treatment, medical personnel observe and analyze the changes in the size, position, and shape of various brain tissues in the brain MRI image, thereby judging whether the brain tissue has been diseased, and formulating the corresponding medical plan. The conclusion drawn after observing the image will be influenced by the subjective experience of the experts and is not objective. Therefore, it has become necessary to try to avoid subjective factors interfering with the diagnosis. This paper proposes an intelligent diagnosis model based on improved deep convolutional neural network (IDCNN). This model introduces integrated support vector machine (SVM) into IDCNN. During image segmentation, if IDCNN has problems such as irrational layer settings, too many parameters, etc., it will make its segmentation accuracy low. This study made a slight adjustment to the structure of IDCNN. First, adjust the number of convolution layers and down-sampling layers in the DCNN network structure, adjust the network’s activation function, and optimize the parameters to improve IDCNN’s non-linear expression ability. Then, use the integrated SVM classifier to replace the original Softmax classifier in IDCNN to improve its classification ability. The simulation experiment results tell that compared with the model before improvement and other classic classifiers, IDCNN improves segmentation results and promote the intelligent diagnosis of brain tissue.

Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification

Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer’s disease (AD).

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data

BACKGROUND

Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

OBJECTIVE

The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

METHODS

First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models.

RESULTS

Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age.

CONCLUSION

Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders

BACKGROUND

Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of Supervised Switching Autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach.

METHODS

Patch-based SSAs models are trained on individual patches extracted from a single 2D slice, independently for Axial, Coronal, and Sagittal anatomical planes of the brain at selected informative locations, exploring different patch sizes and network parameterizations. Then, models perform binary class prediction - healthy (CDR = 0) or AD-demented (CDR > 0) - on test data at patch level. The final subject classification is performed employing a majority rule from the ensemble of patch predictions. In addition, relevant regions are identified, by computing accuracy densities from patch-level predictions, and analyzed, supported by Atlas-based regional definitions.

RESULTS

Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also obtained.

CONCLUSIONS

SSAs provided well-ranked accuracy performance among previous classification proposals, including feature-engineered and feature learning based methods, using only one scan slice per subject, instead of the whole 3D volume, as it is conventionally done. In addition, regions identified as relevant by SSAs' were, in most part, coherent or partially coherent in regard to relevant regions reported on previous works. These regions were also associated with findings from medical knowledge, which gives value to our methodology as a potential analytical aid for disease understanding.

Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine

In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervised learning algorithms were used to train classifiers in which the accuracies are being compared. The database used is from The Alzheimer's Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting.

Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and region-based active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L₂-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness.