First Report and Characterization of the mcr-1 Positive Multidrug-Resistant Escherichia coli Strain Isolated from Pigs in Croatia.

The emergence and rapid spread of the plasmid-mediated colistin-resistant mcr-1 gene introduced a serious threat to public health. In 2021, a multi-drug resistant, mcr-1 positive Escherichia coli EC1945 strain, was isolated from pig caecal content in Croatia. Antimicrobial susceptibility testing and whole genome sequencing were performed. Bioinformatics tools were used to determine the presence of resistance genes, plasmid Inc groups, serotype, sequence type, virulence factors, and plasmid reconstruction. The isolated strain showed phenotypic and genotypic resistance to nine antimicrobial classes. It was resistant to colistin, gentamicin, ampicillin, cefepime, cefotaxime, ceftazidime, sulfamethoxazole, chloramphenicol, nalidixic acid, and ciprofloxacin. Antimicrobial resistance genes included mcr-1, blaTEM-1B, blaCTX-M-1, aac(3)-IId, aph(3')-Ia, aadA5, sul2, catA1, gyrA (S83L, D87N), and parC (A56T, S80I). The mcr-1 gene was located within the conjugative IncX4 plasmid. IncI1, IncFIB, and IncFII plasmids were also detected. The isolate also harbored 14 virulence genes and was classified as ST744 and O101:H10. ST744 is a member of the ST10 group which includes commensal, extraintestinal pathogenic E. coli isolates that play a crucial role as a reservoir of genes. Further efforts are needed to identify mcr-1-carrying E. coli isolates in Croatia, especially in food-producing animals to identify such gene reservoirs.

Results of the 2020 Genomic Proficiency Test for the network of European Union Reference Laboratory for Antimicrobial Resistance assessing whole-genome-sequencing capacities.

The global surveillance and outbreak investigation of antimicrobial resistance (AMR) is amidst a paradigm shift from traditional biology to bioinformatics. This is due to developments in whole-genome-sequencing (WGS) technologies, bioinformatics tools, and reduced costs. The increased use of WGS is accompanied by challenges such as standardization, quality control (QC), and data sharing. Thus, there is global need for inter-laboratory WGS proficiency test (PT) schemes to evaluate laboratories' capacity to produce reliable genomic data. Here, we present the results of the first iteration of the Genomic PT (GPT) organized by the Global Capacity Building Group at the Technical University of Denmark in 2020. Participating laboratories sequenced two isolates and corresponding DNA of Salmonella enterica, Escherichia coli and Campylobacter coli, using WGS methodologies routinely employed at their laboratories. The participants' ability to obtain consistently good-quality WGS data was assessed based on several QC WGS metrics. A total of 21 laboratories from 21 European countries submitted WGS and meta-data. Most delivered high-quality sequence data with only two laboratories identified as overall underperforming. The QC metrics, N50 and number of contigs, were identified as good indicators for high-sequencing quality. We propose QC thresholds for N50 greater than 20 000 and 25 000 for Campylobacter coli and Escherichia coli, respectively, and number of contigs >200 bp greater than 225, 265 and 100 for Salmonella enterica, Escherichia coli and Campylobacter coli, respectively. The GPT2020 results confirm the importance of systematic QC procedures, ensuring the submission of reliable WGS data for surveillance and outbreak investigation to meet the requirements of the paradigm shift in methodology.

CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data.

Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this article, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive (AR) model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to time-series regression and classification tasks for Alzheimer's disease progression modeling, intensive care unit (ICU) mortality rate prediction, human activity recognition, and event-based digit recognition, where the proposed model based on a gated recurrent unit (GRU) in all cases achieves significantly better predictive performance than the state-of-the-art methods using RNNs, GRUs, and long short-term memory (LSTM) networks.

Robust parametric modeling of Alzheimer's disease progression.

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

Inflammatory pathway analytes predicting rapid cognitive decline in MCI stage of Alzheimer's disease.

OBJECTIVE: To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration. METHODS: A longitudinal study of MCI-AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty-three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR-SB change over two years (≥3 points). RESULTS: Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL-10 pathway dysregulation. The ROC curves for ≥3 points change in CDR-SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different. INTERPRETATION: Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL-10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI-AD. CCL2's utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline.

Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial.

OBJECTIVE. The purpose of this study is to establish whether texture analysis and densitometry are complementary quantitative measures of chronic obstructive pulmonary disease (COPD) in a lung cancer screening setting. MATERIALS AND METHODS. This was a retrospective study of data collected prospectively (in 2004-2010) in the Danish Lung Cancer Screening Trial. The texture score, relative area of emphysema, and percentile density were computed for 1915 baseline low-dose lung CT scans and were evaluated, both individually and in combination, for associations with lung function (i.e., forced expiratory volume in 1 second as a percentage of predicted normal [FEV1% predicted]), diagnosis of mild to severe COPD, and prediction of a rapid decline in lung function. Multivariate linear regression models with lung function as the outcome were compared using the likelihood ratio test or the Vuong test, and AUC values for diagnostic and prognostic capabilities were compared using the DeLong test. RESULTS. Texture showed a significantly stronger association with lung function (p < 0.001 vs densitometric measures), a significantly higher diagnostic AUC value (for COPD, 0.696; for Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1, 0.648; for GOLD grade 2, 0.768; and for GOLD grade 3, 0.944; p < 0.001 vs densitometric measures), and a higher but not significantly different association with lung function decline. In addition, only texture could predict a rapid decline in lung function (AUC value, 0.538; p < 0.05 vs random guessing). The combination of texture and both densitometric measures strengthened the association with lung function and decline in lung function (p < 0.001 and p < 0.05, respectively, vs texture) but did not improve diagnostic or prognostic performance. CONCLUSION. The present study highlights texture as a promising quantitative CT measure of COPD to use alongside, or even instead of, densitometric measures. Moreover, texture may allow early detection of COPD in subjects who undergo lung cancer screening.

The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study.

Hippocampal volume and shape are known magnetic resonance imaging biomarkers of neurodegeneration. Recently, hippocampal texture has been shown to improve prediction of dementia in patients with mild cognitive impairment, but it is unknown whether texture adds prognostic information beyond volume and shape and whether the predictive value extends to cognitively healthy individuals. Using 510 subjects from the Rotterdam Study, a prospective, population-based cohort study, we investigated if hippocampal volume, shape, texture, and their combination were predictive of dementia and determined how predictive performance varied with time to diagnosis and presence of early clinical symptoms of dementia. All features showed significant predictive performance with the area under the receiver operating characteristic curve ranging from 0.700 for texture alone to 0.788 for the combination of volume and texture. Although predictive performance extended to those without objective cognitive complaints or mild cognitive impairment, performance decreased with increasing follow-up time. We conclude that a combination of multiple hippocampal features on magnetic resonance imaging performs better in predicting dementia in the general population than any feature by itself.

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p < 0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger area under the receiver operating characteristic curve (AUC) of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals. This paper shows that built-in handling of missing values in training an LSTM network benefits the application of RNNs in neurodegenerative disease progression modeling in longitudinal cohorts.

Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning.

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.

Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a lung disease that can be quantified using chest computed tomography scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multicenter dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multicenter classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains can further improve the results. To encourage further research into transfer learning methods for the classification of COPD, upon acceptance of this paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

BACKGROUND: The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. NEW METHOD: We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. RESULTS: The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. COMPARISON WITH EXISTING METHOD(S): The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. CONCLUSIONS: Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data.

Subclinical depressive symptoms during late midlife and structural brain alterations: A longitudinal study of Danish men born in 1953.

We explored whether depressive symptoms measured three times during midlife were associated with structural brain alterations quantified using magnetic resonance imaging measurements of volume, cortical thickness, and intensity texture. In 192 men born in 1953 with depressive symptoms measured at age 51, 56, and 59 years, magnetic resonance imaging was performed at age 59. All data processing was performed using the Freesurfer software package except for the texture-scores that were computed using in-house software. Structural brain alterations and associations between depressive symptoms and brain structure outcomes were tested using Pearson's correlation, t test, and linear regression. Depressive symptoms at age 51 showed clear inverse correlations with total gray matter, pallidum, and hippocampal volume with the strongest estimate for hippocampal volume (r = -.22, p < .01). After exclusion of men (n = 3) with scores in the range of clinical depression the inverse correlation between depressive symptoms and hippocampal volume became insignificant (r = -13, p = .08). Depressive symptoms at age 59 correlated positively with hippocampal and amygdala texture-potential early markers of atrophy. Inverse relations with total gray matter and pallidum volumes lost significance when the analysis was adjusted for intracranial volume. In men, depressive symptoms at age 51 were associated with a reduced volume of the hippocampus at age 59 independent of later symptoms. Amygdala and hippocampal textures might be the early markers for brain alterations associated with depression in midlife.

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry.

This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI.

Deformation-based atrophy computation by surface propagation and its application to Alzheimer's disease.

Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)-a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer's disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer's disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.

Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions.

In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter- and intra-subject image registration.

Early detection of Alzheimer's disease using MRI hippocampal texture.

Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1-weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and subsequently applied to score independent data sets from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and the Metropolit 1953 Danish Male Birth Cohort (Metropolit). Hippocampal texture was superior to volume reduction as predictor of MCI-to-AD conversion in ADNI (area under the receiver operating characteristic curve [AUC] 0.74 vs. 0.67; DeLong test, p = 0.005), and provided even better prognostic results in AIBL (AUC 0.83). Hippocampal texture, but not volume, correlated with Addenbrooke's cognitive examination score (Pearson correlation, r = -0.25, p < 0.001) in the Metropolit cohort. The hippocampal texture marker correlated with hippocampal glucose metabolism as indicated by fluorodeoxyglucose-positron emission tomography (Pearson correlation, r = -0.57, p < 0.001). Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly. Our study highlights the presence of hippocampal texture abnormalities in MCI, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI.

BACKGROUND: Alzheimer's disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects. METHODS: A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume. RESULTS: We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period. CONCLUSION: Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

Texture-based analysis of COPD: a data-driven approach.

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated regions of interest (ROIs). A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN ) classifier. The distance between two ROIs in the kNN classifier is computed as the textural dissimilarity between the ROIs, where the ROI texture is described by histograms of filter responses from a multi-scale, rotation invariant Gaussian filter bank. The method was trained on 400 images from a lung cancer screening trial and subsequently applied to classify 200 independent images from the same screening trial. The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density. The proposed measure achieved an area under the receiver operating characteristic curve (AUC) of 0.713 whereas the best performing density measure achieved an AUC of 0.598. Further, the proposed measure is as reproducible as the density measures, and there were indications that it correlates better with lung function and is less influenced by inspiration level.

Dissimilarity-based classification of anatomical tree structures.

A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment between the branch feature vectors representing those trees. Hereby, localized information in the branches is collectively used in classification and variations in feature values across the tree are taken into account. An approximate anatomical correspondence between matched branches can be achieved by including anatomical features in the branch feature vectors. The proposed approach is applied to classify airway trees in computed tomography images of subjects with and without chronic obstructive pulmonary disease (COPD). Using the wall area percentage (WA%), a common measure of airway abnormality in COPD, as well as anatomical features to characterize each branch, an area under the receiver operating characteristic curve of 0.912 is achieved. This is significantly better than computing the average WA%.