Abnormal Regions Research Articles

Response characteristics of objective perimetry in persons living with epilepsy

PurposeTo investigate safety and visual-field changes in people with epilepsy undergoing multifocal Pupillographic Objective Perimetry (mfPOP). Methods15 people with epilepsy and 15 controls underwent mfPOP in the context of routine clinical EEG testing. Safety measures comprised the proportion of participants developing an aura or seizure, a photoparoxysmal response, or increased epileptiform activity on their EEG during mfPOP. Pupil responses were obtained concurrently from 44 regions/field of each eye. Changes in standardised amplitude of constriction and time-to-peak were compared between people with generalised and focal epilepsy, and controls. ResultsNo participant developed an epileptic aura or clinical seizure during (or after) testing. One participant demonstrated EEG evidence of a focal subclinical seizure which began before mfPOP testing and continued unchanged during testing. Regional field sensitivities were increased in people with generalised epilepsy (+3.80 ± 1.43 dB compared to controls) but were reduced in individuals taking antiepileptic medication (−4.04 ± 1.74 dB). An extra delay of 24.9 ± 10.2 ms was seen in the time-to-peak of the responses in people with focal epilepsy. Based on receiver-operating characteristic analyses, discrimination of people with epilepsy from controls was greatest when using the 4 to 10 most abnormal visual field regions of each eye (%AUC 77.3 ± 9.70). SignificanceIn the absence of any safety signal, mfPOP appears harmless in people with epilepsy. The observed abnormalities in per-region sensitives and delays suggest that mfPOP may provide significant new insights into the study of epilepsy.

Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data.

Functional Connectivity Changes in Multiple-Frequency Bands in Acute Basal Ganglia Ischemic Stroke Patients: A Machine Learning Approach.

Purpose Several functional magnetic resonance imaging (fMRI) studies have investigated the resting-state functional connectivity (rs-FC) changes in the primary motor cortex (M1) in patients with acute basal ganglia ischemic stroke (BGIS). However, the frequency-specific FC changes of M1 in acute BGIS patients are still unclear. Our study was aimed at exploring the altered FC of M1 in three frequency bands and the potential features as biomarkers for the identification by using a support vector machine (SVM). Methods We included 28 acute BGIS patients and 42 healthy controls (HCs). Seed-based FC of two regions of interest (ROI, bilateral M1s) were calculated in conventional, slow-5, and slow-4 frequency bands. The abnormal voxel-wise FC values were defined as the features for SVM in different frequency bands. Results In the ipsilesional M1, the acute BGIS patients exhibited decreased FC with the right lingual gyrus in the conventional and slow-4 frequency band. Besides, the acute BGIS patients showed increased FC with the right medial superior frontal gyrus (SFGmed) in the conventional and slow-5 frequency band and decreased FC with the left lingual gyrus in the slow-5 frequency band. In the contralesional M1, the BGIS patients showed lower FC with the right SFGmed in the conventional frequency band. The higher FC values with the right lingual gyrus and left SFGmed were detected in the slow-4 frequency band. In the slow-5 frequency band, the BGIS patients showed decreased FC with the left calcarine sulcus. SVM results showed that the combined features (slow-4+slow-5) had the highest accuracy in classification prediction of acute BGIS patients, with an area under curve (AUC) of 0.86. Conclusion Acute BGIS patients had frequency-specific alterations in FC; SVM is a promising method for exploring these frequency-dependent FC alterations. The abnormal brain regions might be potential targets for future researchers in the rehabilitation and treatment of stroke patients.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.

PurposeThe purpose of this study was to improve the diagnostic ability of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by understanding the frequency and pattern of artifacts seen on the p-maps of healthy control (HC) eyes resembling glaucomatous damage.MethodsRNFL p-maps were generated from wide-field OCT cube scans of 2 groups of HC eyes, 200 from a commercial normative group (HC-norm) and 54 from a prospective study group, as well as from 62 patient eyes, which included 32 with early glaucoma (EG). These 32 EG eyes had 24-2 mean deviation (MD) better than −6 dB and perimetric glaucoma as defined by 24-2 and 10-2 criteria. For the HC groups, “glaucoma-like” arcuates were defined as any red region near the temporal half of the disc.ResultsSeven percent of the 200 HC-norm and 11% of the 54 HC RNFL p-maps satisfied the definition of “glaucoma-like,” as did all the patients’ p-maps. The HC p-maps showed two general patterns of abnormal regions, “arcuate” and “temporal quadrant,” and these patterns resembled those seen on some of the RNFL p-maps of the EG eyes. A “vertical midline” rule, which required the abnormal region to cross the vertical midline through the fovea, had a specificity of >99%, and a sensitivity of 75% for EG and 93% for moderate to advanced eyes.ConclusionsGlaucoma-like artifacts on RNFL p-maps are relatively common and can masquerade as arcuate and/or widespread/temporal damage.Translational RelevanceA vertical midline rule had excellent specificity. However, other OCT information is necessary to obtain high sensitivity, especially in eyes with early glaucoma.

Identifying Changes of Brain Regional Homogeneity and Cingulo-Opercular Network Connectivity in First-Episode, Drug-Naïve Depressive Patients With Suicidal Ideation.

ObjectiveAdult patients with major depressive disorder (MDD) may not actively reveal their suicidal ideation (SI). Therefore, this study is committed to finding the alterations in the cingulo-opercular network (CON) that are closely related to SI with multi-imaging methods, thus providing neuroimaging basis for SI.MethodA total of 198 participants (129 MDD patients and 69 healthy controls) were recruited and evaluated with the Montgomery–Asberg Depression Rating Scale (MADRS). The healthy individuals formed the HC group, while the MDD patients were subdivided into no SI MDD (NSI, n = 32), mild SI MDD (MSI, n = 64), and severe SI MDD (SSI, n = 33) according to their MADRS item 10. We obtained MRI data of all participants and applied regional homogeneity (ReHo) analysis to verify a previous finding that links CON abnormality to SI. In addition, we employed the structural covariance network (SCN) analysis to investigate the correlation between abnormal structural connectivity of CON and SI severity.ResultsCompared to those of the HC group, MDD ReHo values and gray matter volume (GMV) were consistently found abnormal in CON. ReHo values and GMV of the right orbital inferior frontal gyrus (ORBinf.R) in the MDD group decreased with the increase of SI. Compared to the HC group, the MDD patients showed enhanced structural connectivity of three pairs of brain regions in CON [ACC.L–left superior frontal gyrus (SFG.L), SFG.L–left middle temporal gyrus (MTG.L), and the SFG.L–left post-central gyrus (PoCG.L)]. Compared with that of the NSI and MSI groups, the structural connectivity of three pairs of brain regions in CON is enhanced in the SSI groups [ORBinf.L–right ventral posterior cingulate gyrus (VPCC.R), VPCC.R–SFG.R, and SFG.R–PoCG.R].ConclusionOur findings showed the distinctive ReHo, GMV, and SCN pattern of CON in MDD patients with SI; and with the severity of suicide, abnormal brain regions increased. Our finding suggested that MDD patients with different severity of SI have different neuroimaging changes.

Neutrosophic set based clustering approach for segmenting abnormal regions in mammogram images

Neutrosophic set based clustering approach for segmenting abnormal regions in mammogram images

Altered structural covariance and functional connectivity of the insula in patients with Crohn's disease.

Crohn's disease (CD) is a clinically chronic inflammatory bowel disease, which has been shown to be closely related to the brain-gut axis dysfunction. Although traditionally considered to be a limbic region, the insula has also been commonly identified as an abnormal brain region in previous CD-related studies. Structural magnetic resonance imaging (MRI) and resting-state functional MRI images were acquired from 45 CD patients in remission and 40 healthy controls (HCs). Three neuroimaging analysis methods including voxel-based morphometry (VBM), structural covariance, and functional connectivity (FC) were applied to investigate structural and functional alterations of the insulae between the CD patients and HCs. Pearson correlation was then used to examine the relationships between neuroimaging findings and clinical symptoms. Compared with the HCs, CD patients exhibited decreased gray matter volume (GMV) in the left dorsal anterior insula (dAI) and bilateral posterior insula (PI). Taking these three areas including the left dAI, right PI, and left PI as regions of interest (ROIs), differences were observed in the structural covariance and FC of the ROI with several regions between the two groups. After controlling for psychological factors, the differences of several regions involved in emotional processing in GMV in the left dAI, the FC of the dAI, and the right PI were not significant. The FC of the parahippocampus/hippocampus with dAI and PI were negatively correlated with the CD activity index (CDAI). We suggest that the insula-centered structural and/or functional changes may be associated with abnormal visceral sensory processing and related emotional responses in CD patients.

Application of optimal clustering and metric learning to patch-based anomaly detection

Anomaly detection in computer vision is a process of identifying observations or events deviated from the norm. Pixel- and patch-based approaches for locating abnormal regions suffer from a tremendously expensive computational complexity due to repeated calculations inherently required. The approach introduced in this letter aims to reducing such computational burden by employing an optimal clustering method for features extracted from a deep network-based encoder, and a metric learning which provides a way to bias the clusters found by the clustering algorithm. A set of loss functions is defined and used for training the encoder. After the training steps, the clustering result obtained from the training dataset is saved and used in the inference phase by measuring distances between the features of the input data and the clusters. The proposed approach has been applied to a patch-based anomaly detection, Patch-SVDD, to clarify the effectiveness of the idea. The experiment carried out with the implementation on MVTec-AD dataset results in improved detection speed by 10∼35% and better detection accuracy for many image classes, as well. In addition, the ablation study conducted proves the validity of the ideas introduced in the proposed approach.

An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

• Unsupervised segmentation of polyps in colonoscopic images. • Superpixel level grouping of pixels using simple linear iterative clustering (SLIC). • Deployment of adaptive Markov random field (MRF) to stitch homogeneous regions with region adjacency graph (RAG). • Polyp segmentation efficiency of 60.77% is achieved in terms of dice co-efficient. In this article, an unsupervised image segmentation method is proposed to segment out polyps in endoscopic images. Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed. The polyps or abnormal regions show markedly different texture and color characteristics in contrast to normal tissues. In our method, the endoscope images are first over-segmented into superpixels . For final refinement, Local binary pattern (LBP) and color features are used in the adaptive MRF. The experimental results show that the proposed method can perfectly localize polyp regions, and it can give a mean dice value of 60.77% in a test set of 612 images taken from 29 different video sequences. Deep learning models require a huge number of training images for tweaking of network parameters. In this context, it is sometimes challenging to deploy a deep model for unseen medical datasets having many segmentation challenges, like complex background, specularity, non-uniform illuminations, etc. Also, manual labeling of a huge number of images is another challenge. That is why we adopted an unsupervised approach in this paper.

Relationship Between Gastrointestinal Transit, Medsger Gastrointestinal Severity, and University of California-Los Angeles Scleroderma Clinical Trial Consortium Gastrointestinal Tract 2.0 Symptoms in Patients With Systemic Sclerosis.

Systemic sclerosis (SSc)-associated gastrointestinal (GI) complications are attributed to a variety of factors, including diet, microbiota dysbiosis, or GI transit abnormalities. Our objective was to examine the contribution of abnormal GI transit to SSc Medsger GI severity scores and/or University of California Los Angeles Scleroderma Clinical Trial Consortium Gastrointestinal Tract (UCLA GIT) 2.0 symptoms. Patients with SSc and GI symptoms (n = 71) and healthy controls (n = 18) underwent whole gut transit (WGT) scintigraphy to assess transit from the esophagus to the colon. The presence of delayed transit and percent emptying in each GI region were measured. We compared the WGT measurements between categories of the Medsger GI severity score (0-4) and across UCLA GIT 2.0 domains and total score (0-3). A total of 88% of patients had >1 abnormal region of the gut on WGT scintigraphy. All patients requiring total parenteral nutrition had delayed small bowel transit, compared to only approximately 11% of patients in other Medsger GI severity groups (P ≤ 0.01). Severe colonic transit delays were more likely in patients with Medsger GI scores of 3 (pseudo-obstruction and/or malabsorption) compared to other Medsger GI groups (P = 0.02). Seventy-percent of these patients had ≤30% colonic emptying at 72 hours. Modest associations were noted between gastroesophageal reflux disease symptoms and delayed esophageal (r = -0.31, P = 0.05) and gastric emptying (r = -0.32, P = 0.05). These data are important in providing evidence that SSc bowel disease affects transit of GI content and that delay in transit accounts in part for both bowel symptoms and Medsger GI severity. Prospective studies examining the benefit of early therapeutic intervention targeting GI transit abnormalities in patients at high risk for severe GI complications are needed.

Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue.

The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner.To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively.We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = −3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003).Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration

The retinal pigment epithelium (RPE) is essential for the survival and function of retinal photoreceptor cells. RPE dysfunction causes various retinal diseases including age-related macular degeneration (AMD). Clinical studies on ES/iPS cell-derived RPE transplantation for RPE dysfunction-triggered diseases are currently underway. Quantification of the diseased RPE area is important to evaluate disease progression or the therapeutic effect of RPE transplantation. However, there are no standard protocols. To address this issue, we developed a 2-step software that enables objective and efficient quantification of RPE-disease area changes by analyzing the early-phase hyperfluorescent area in fluorescein angiography (FA) images. We extracted the Abnormal region. This extraction was based on deep learning-based discrimination. We scored the binarized extracted area using an automated program. Our program’s performance for the same eye from the serial image captures was within 3.1 ± 7.8% error. In progressive AMD, the trend was consistent with human assessment, even when FA images from two different visits were compared. This method was applicable to quantifying RPE-disease area changes over time, evaluating iPSC-RPE transplantation images, and a disease other than AMD. Our program may contribute to the assessment of the clinical course of RPE-disease areas in routine clinics and reduce the workload of researchers.

Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2years.

Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12months of age in surface area. By 24months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.

Divergence in the Relativistic Mean Field Formalism: A Case Study of the Ground State Properties of the Decay Chain of 214,216,218U Isotopes

A new α-emitting 214U has been recently observed experimentally. This opens the window to theoretically investigate the ground-state properties of the lightest known even–even neutron deficient 214,216,218U isotopes and to examine α-particle clustering around the shell closure. The decay half-lives are calculated within the preformed cluster-decay model (PCM). To obtain the α-daughter interaction potential, the RMF densities are folded with the newly developed R3Y and the well-known M3Y NN potentials for comparison. The alpha preformation probability (Pα) is calculated from the analytic formula of Deng and Zhang. The WKB approximation is employed for the calculation of the transmission probability. The individual binding energies (BE) for the participating nuclei are estimated from the relativistic mean-field (RMF) formalism and those from the finite range droplet model (FRDM) as well as WS3 mass tables. In addition to Z=84, the so-called abnormal enhancement region, i.e., 84≤Z≤90 and N<126, is normalised by an appropriately fitted neck-parameter ΔR. On the other hand, the discrepancy sets in due to the shell effect at (and around) the proton magic number Z=82 and 84, and thus a higher scaling factor ranging from 10−8–10−5 is required. Additionally, in contrast with the experimental binding energy data, large deviations of about 5–10 MeV are evident in the RMF formalism despite the use of different parameter sets. An accurate prediction of α-decay half-lives requires a Q-value that is in proximity with the experimental data. In addition, other microscopic frameworks besides RMF could be more reliable for the mass region under study. α-particle clustering is largely influenced by the shell effect.

Programmed for Automatic Bone Disorder Clustering Based on Cumulative Calcium Prediction for Feature Extraction.

The prediction of bone disorders varies between ortho-physicians. A precise bone disorder cataloging system is proposed based on a renewed method for estimating calcium value from a radiological image of the bone. A deliberate method was employed, the binning technique, for the input image which divides the input image into non-overlapping blocks to obtain accurate calcium volume estimation. In this proposed approach, the input image undergoes two stages of the process. In stage 1, input image preprocessing is accomplished with median filtering to eliminate the unwanted noise and it increases the quality of the image. Further, the processed image is fed to the Otsu-thresholding-segmentation method to highlight the affected regions from the processed bone image. The LBP (Local Binary Pattern) is a technique implemented to pull out the feature vector alone from the input image. Calcium value is estimated from abnormal regions from the segmented bone image and with the help of extracted texture features, the calcium concentration is obtained. MSVM (Multi-class Support Vector Machine) technique is applied to categorize as normal, osteoporosis, and osteopenia. In stage 2, the entire input is divided into 4 x 4 bins and preprocessing, segmentation, feature extraction, and calcium estimation process were applied similar to stage I to each bin separately and the calcium values of all bins are added together. Finally, stage 1 and stage 2 calcium values are summed up to obtain a more precise calcium estimation of the input image the feature vectors which were pull-out from others. The result can prove that the proposed binning technique is best for the bone disorder classification system which attained the greater accuracy of 97.4% and sensitivity of 98.3% when compared with and without binning technique. Validation of the results was performed with bone images, and these bone images were declared by the physician as bone disorder-affected images. The success rate of the bone disorder prediction is 80%.

Cerebral perfusion in posterior reversible encephalopathy syndrome measured with arterial spin labeling MRI

Background and purposeThe pathophysiologic basis of posterior reversible encephalopathy syndrome (PRES) remains controversial. Hypertension (HTN)-induced autoregulatory failure with subsequent hyperperfusion is the leading hypothesis, whereas alternative theories suggest vasoconstriction-induced hypoperfusion as the underlying mechanism. Studies using contrast-based CT and MR perfusion imaging have yielded contradictory results supporting both ideas. This work represents one of the first applications of arterial spin labeling (ASL) to evaluate cerebral blood flow (CBF) changes in PRES. Materials and methodsAfter obtaining Institutional Review Board approval, MRI reports at our institution from 07/2015 to 09/2020 were retrospectively searched and reviewed for mention of “PRES” and “posterior reversible encephalopathy syndrome.” Of the resulting 103 MRIs (performed on GE 1.5 Tesla or 3 Tesla scanners), 20 MRIs in 18 patients who met the inclusion criteria of clinical and imaging diagnosis of PRES and had diagnostic-quality pseudocontinuous ASL scans were included. Patients with a more likely alternative diagnosis, technically non-diagnostic ASL, or other intracranial abnormalities limiting assessment of underlying PRES features were excluded. Perfusion in FLAIR-affected brain regions was qualitatively assessed using ASL and characterized as hyperperfusion, normal, or hypoperfusion. Additional quantitative analysis was performed by measuring average gray matter CBF in abnormal versus normal brain regions. ResultsHTN was the most common PRES etiology (65%). ASL showed hyperperfusion in 13 cases and normal perfusion in 7 cases. A hypoperfusion pattern was not identified. Quantitative analysis of gray matter CBF among patients with visually apparent hyperperfusion showed statistically higher perfusion in affected versus normal appearing brain regions (median CBF 100.4 ml/100 g-min vs. 61.0 ml/ 100 g-min, p < 0.001). ConclusionElevated ASL CBF was seen in the majority (65%) of patients with PRES, favoring the autoregulatory failure hypothesis as a predominant mechanism. Our data support ASL as a practical way to assess and noninvasively monitor cerebral perfusion in PRES that could potentially alter management strategies.

Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop.

Accurate classification and localization of abnormalities in chest X-rays play an important role in clinical diagnosis and treatment planning. Building a highly accurate predictive model for these tasks usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their prior knowledge, as well as radiomic features, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised knowledge-augmented contrastive learning framework, that simultaneously performs disease classification and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation. Specifically, we first apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic features to mutually reinforce each other. Their contrasting yields knowledge-augmented representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both classification and localization tasks.

Functional Connectivity Density in the Sensorimotor Area is Associated with Sleep Latency in Patients with Primary Insomnia.

ObjectiveIt is unclear whether the patterns of functional connectivity density (FCD) differ between patients with primary insomnia (PI) and healthy subjects. In the present study, we investigated the features of FCD in patients with PI using resting-state functional MRI (rsfMRI).MethodsrsfMRI datasets of 32 patients with PI and 34 healthy controls (HCs) were obtained using a 3-Tesla scanner. FCD analysis was performed to compare voxels with abnormal whole-brain functional connectivity with other voxels among patients with PI and HCs. Abnormal brain regions were then used as seed points for FC analysis.ResultsCompared with HCs, patients with PI exhibited significantly decreased FCD in the left medial frontal gyrus and increased FCD in the left supplementary motor area (SMA). With the left medial frontal gyrus as the seed point, patients with PI showed decreased FC between the left medial frontal gyrus and the left fusiform gyrus compared with HCs. With the left SMA as the seed point, patients with PI exhibited increased FC between the left SMA and the right anterior cingulate gyrus. Correlation analysis revealed that the increased FCD values in the left SMA were positively correlated with sleep latency in patients with PI.ConclusionDefault-mode network and SMA dysfunctions may be related to the pathophysiology of PI.

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network.

An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.

Simple SummaryBreast cancer is leading cancer increases the death rate in women. Early diagnosis of breast cancer in women can save their lives. The current study proposed a novel scheme to detect architectural distortion from mammogram images to predict breast cancer using a deep learning approach. Results are evaluated on a public and a private dataset which may help to improve the diagnostic ability of breast cancer of radiologists and doctors in daily clinical routines. Furthermore, the proposed method achieved maximum accuracy as compared with previous approaches. This study can be interesting and valuable in the healthcare predictive modeling domain and will add a real contribution to society.Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.