Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201

β2-Adrenergic receptor stimulation improves endothelial progenitor cell-mediated ischemic neoangiogenesis

Endothelial progenitor cells (EPCs) are present in the systemic circulation and home to sites of ischemic injury where they promote neoangiogenesis. β2-Adrenergic receptor (β2AR) plays a critical role in vascular tone regulation and neoangiogenesis

The association between environmental exposures during childhood and the subsequent development of Crohn's Disease: A score analysis approach

Background Environmental factors during childhood are thought to play a role in the aetiology of Crohn's Disease (CD). In South Africa, recently published work based on an investigation of 14 childhood environmental exposures during 3 age intervals (0-5, 6-10 and 11-18 years) has provided insight into the role of timing of exposure in the future development of CD. The 'overlapping' contribution of the investigated variables however, remains unclear. The aim of this study was to perform a post hoc analysis using this data and investigate the extent to which each variable contributes to the subsequent development of CD relative to each aforementioned age interval, based on a score analysis approach. Methods Three methods were used for the score analysis. Two methods employed the subgrouping of one or more (similar) variables (methods A and B), with each subgroup assigned a score value weighting equal to one. For comparison, the third approach (method 0) involved no grouping of the 14 variables. Thus, each variable held a score value of one. Results Results of the score analysis (Method 0) for the environmental exposures during 3 age intervals (0-5, 6-10 and 11-18 years) revealed no significant difference between the case and control groups. By contrast, results from Method A and Method B revealed a significant difference during all 3 age intervals between the case and control groups, with cases having significantly lower exposure scores (approximately 30% and 40% lower, respectively). Conclusion Results from the score analysis provide insight into the 'compound' effects from multiple environmental exposures in the aetiology of CD.IS

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH ($\rho$=.85) and hippocampal volumes (r=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH ($\rho$=.85) and hippocampal volumes (r=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg

Evaluation of Directed Graph-Mapping in Complex Atrial Tachycardias

Objectives: Directed graph-mapping (DGM) is a novel operator-independent automatic tool that can be applied to the identification of the atrial tachycardia (AT) mechanism. In the present study, for the first time, DGM was applied in complex AT cases, and diagnostic accuracy was evaluated. Background: Catheter ablation of ATs still represents a challenge, as the identification of the correct mechanism can be difficult. New algorithms for high-density activation mapping (HDAM) render an easier acquisition of more detailed maps; however, understanding of the mechanism and, thus, identification of the ablation targets, especially in complex cases, remains strongly operator-dependent. Methods: HDAMs acquired with the latest algorithm (COHERENT version 7, Biosense Webster, Irvine, California) were interpreted offline by 4 expert electrophysiologists, and the acquired electrode recordings with corresponding local activation times (LATs) were analyzed by DGM (also offline). Entrainment maneuvers (EM) were performed to understand the correct mechanism, which was then confirmed by successful ablation (13 cases were centrifugal, 10 cases were localized re-entry, 22 cases were macro–re-entry, and 6 were double-loops). In total, 51 ATs were retrospectively analyzed. We compared the diagnoses made by DGM were compared with those of the experts and with additional EM results. Results: In total, 51 ATs were retrospectively analyzed. Experts diagnosed the correct AT mechanism and location in 33 cases versus DGM in 38 cases. Diagnostic accuracy varied according to different AT mechanisms. The 13 centrifugal activation patterns were always correctly identified by both methods; 2 of 10 localized reentries were identified by the experts, whereas DGM diagnosed 7 of 10. For the macro–re-entries, 12 of 22 were correctly identified using HDAM versus 13 of 22 for DGM. Finally, 6 of 6 double-loops were correctly identified by the experts, versus 5 of 6 for DGM. Conclusions: Even in complex cases, DGM provides an automatic, fast, and operator-independent tool to identify the AT mechanism and location and could be a valuable addition to current mapping technologies. © 2021 The Authors.Dr. Lorenzo is an employee of Biosense Webster. Dr. Goedgebeur is funded with a research grant of the Research Foundation Flanders/Fonds voor Wetenschappelijk Onderzoek (FWO). Dr. Strisciuglio is supported by a research grant from the Cardiopath PhD program. Dr. el Haddad is a consultant for Biosense Webster. Dr. Duytschaever is a consultant for Biosense Webster. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

Cyclic vomiting syndrome in children: a nationwide survey of current practice on behalf of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP) and Italian Society of Pediatric Neurology (SINP)

Background: Cyclic Vomiting Syndrome (CVS) is a rare functional gastrointestinal disorder, which has a considerable burden on quality of life of both children and their family. Aim of the study was to evaluate the diagnostic modalities and therapeutic approach to CVS among Italian tertiary care centers and the differences according to subspecialties, as well as to explore whether potential predictive factors associated with either a poor outcome or a response to a specific treatment. Methods: Cross-sectional multicenter web-based survey involving members of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP) and Italian Society of Pediatric Neurology (SINP). Results: A total of 67 responses were received and analyzed. Most of the respondent units cared for less than 20 patients. More than half of the patients were referred after 3 to 5 episodes, and a quarter after 5 attacks. We report different diagnostic approaches among Italian clinicians, which was particularly evident when comparing gastroenterologists and neurologists. Moreover, our survey demonstrated a predilection of certain drugs during emetic phase according to specific clinic, which reflects the cultural background of physicians. Conclusion: In conclusion, our survey highlights poor consensus amongst clinicians in our country in the diagnosis and the management of children with CVS, raising the need for a national consensus guideline in order to standardize the practice