Histogram-based Analysis Research Articles

Histogram-based analysis in progressive pulmonary fibrosis: relationships between pulmonary functional tests and HRCT indexes.

To investigate relationships between histogram-based high-resolution CT (HRCT) indexes and pulmonary function tests (PFTs) in interstitial lung diseases. Forty-nine patients having baseline and 1-year HRCT examinations and PFTs were investigated. Histogram-based HRCT indexes were calculated; strength of associations with PFTs was investigated using Pearson correlation. Patients were divided into progressive and non-progressive groups. HRCT indexes were compared between the two groups using the U-test; within each group, baseline and follow-up Wilcoxon analysis was performed. Receiver operating characteristic analysis was used for predicting disease progression. At baseline, moderate correlations were observed considering kurtosis and diffusion capacity of the lungs for carbon monoxide (DLCO) (r = 0.54) and skewness and DLCO (r = 0.559), whereas weak but significant correlations were observed between forced vital capacity and kurtosis (r = 0.368, p = 0.009) and forced vital capacity and skewness (r = 0.391, p = 0.005). Negative correlations were reported between HAA% and PFTs (from r = -0.418 up to r = -0.507). At follow-up correlations between quantitative indexes and PFTs were also moderate, except for high attenuation area (HAA)% -700 and DLCO (r = -0.397). In progressive subgroup, moderate and strong correlations were found between DLCO and HRCT indexes (r = 0.595 kurtosis, r = 0.672 skewness, r=-0. 598 HAA% -600 and r = -0.626 HAA% -700). At follow-up, we observed significant differences between the two groups for kurtosis (p = 0.029), HAA% -600 (p = 0.04) and HAA% -700 (p = 0.02). To predict progression, ROC analysis reported sensitivity of 90.9% and specificity of 51.9% using a threshold value of δ kurtosis <0.03. At one year, moderate correlations suggest that progression could be assessed through HRCT quantification. This study promotes histogram-based HRCT indexes in the assessment of progressive pulmonary fibrosis.

Histogram-Based Analysis of Low- and High-Grade Glioma and Its Surrounding Edema Using Arterial Spin Labeling Magnetic Resonance Imaging

A glioma is a type of intra-axial brain tumor originating from the glial cells. Making up about one-third of all brain tumors, a timely diagnosis alongside correct grading and subsequent therapy planning is crucial. Magnetic Resonance Imaging is an established method for the diagnosis of tumors. Arterial Spin Labeling (ASL) Perfusion Imaging allows for the non-contrast enhanced visualization of tumor hyper- or hypoperfusion. Commonly, cell swelling occurs around the tumor that causes edema, which subsequently puts healthy tissue at risk by potentially reducing regional perfusion. The patient collective in this study consists of 495 patients (501 scans) with histopathologically confirmed grade II-IV diffuse gliomas. The aim of this study was to evaluate the potential of histogram analysis of the ASL data to find biomarkers for the pathological diagnosis, grading, MGMT, and mutation status of the tumors as well as the analysis of tumor-surrounding edema. The analysis showed statistically significant results for the pathological diagnosis and grading but not for MGMT status or mutation. The differentiation between tumor and edema showed highly significant results yet did not show differences between edema and perfusion on the contralateral hemisphere.

Micro-CT Features of Lung Consolidation, Collagen Deposition and Inflammation in Experimental RSV Infection Are Aggravated in the Absence of Nrf2.

Severe respiratory syncytial virus (RSV) infections in early life have been linked to the development of chronic airway disease. RSV triggers the production of reactive oxygen species (ROS), which contributes to inflammation and enhanced clinical disease. NF-E2-related factor 2 (Nrf2) is an important redox-responsive protein that helps to protect cells and whole organisms from oxidative stress and injury. The role of Nrf2 in the context of viral-mediated chronic lung injury is not known. Herein, we show that RSV experimental infection of adult Nrf2-deficient BALB/c mice (Nrf2-/-; Nrf2 KO) is characterized by enhanced disease, increased inflammatory cell recruitment to the bronchoalveolar compartment and a more robust upregulation of innate and inflammatory genes and proteins, compared to wild-type Nrf2+/+ competent mice (WT). These events that occur at very early time points lead to increased peak RSV replication in Nrf2 KO compared to WT mice (day 5). To evaluate longitudinal changes in the lung architecture, mice were scanned weekly via high-resolution micro-computed tomography (micro-CT) imaging up to 28 days after initial viral inoculation. Based on micro-CT qualitative 2D imaging and quantitative reconstructed histogram-based analysis of lung volume and density, we found that RSV-infected Nrf2 KO mice developed significantly greater and prolonged fibrosis compared to WT mice. The results of this study underscore the critical role of Nrf2-mediated protection from oxidative injury, not only in the acute pathogenesis of RSV infection but also in the long-term consequences of chronic airway injury.

Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

BackgroundTo assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis.MethodsA total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed.ResultsThe aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively.ConclusionsWhole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

Remote photonic sensing of action potential in mammalian nerve cells via histogram-based analysis of temporal spatial acoustic vibrations.

Label free and remote action potential detection in neurons can be of great importance in the neuroscience research field. This paper presents a novel label free imaging modality based on the detection of temporal vibrations of speckle patterns illuminating the sample. We demonstrated the feasibility of detecting action potentials originating from spontaneous and stimulated activity in cortical cell culture. The spatiotemporal vibrations of isolated cortical cells were extracted by illuminating the culture with a laser beam while the vibrations of the random back scattered secondary speckle patterns are captured by a camera. The postulated action potentials were estimated following correlation-based analysis on the captured vibrations, where the variance deviation of the signal from a Gaussian distribution is directly associated with the action potential events. The technique was validated in a series of experiments in which the optical signals were acquired concurrently with microelectrode array (MEA) recordings. Our results demonstrate the ability of detecting action potential events in mammalian cells remotely via extraction of acoustic vibrations.

Calculating the force-dependent unbinding rate of biological macromolecular bonds from force-ramp optical trapping assays

The non-covalent biological bonds that constitute protein–protein or protein–ligand interactions play crucial roles in many cellular functions, including mitosis, motility, and cell–cell adhesion. The effect of external force (F) on the unbinding rate ({k}_{text{off}}left(Fright)) of macromolecular interactions is a crucial parameter to understanding the mechanisms behind these functions. Optical tweezer-based single-molecule force spectroscopy is frequently used to obtain quantitative force-dependent dissociation data on slip, catch, and ideal bonds. However, analyses of this data using dissociation time or dissociation force histograms often quantitatively compare bonds without fully characterizing their underlying biophysical properties. Additionally, the results of histogram-based analyses can depend on the rate at which force was applied during the experiment and the experiment’s sensitivity. Here, we present an analytically derived cumulative distribution function-like approach to analyzing force-dependent dissociation force spectroscopy data. We demonstrate the benefits and limitations of the technique using stochastic simulations of various bond types. We show that it can be used to obtain the detachment rate and force sensitivity of biological macromolecular bonds from force spectroscopy experiments by explicitly accounting for loading rate and noisy data. We also discuss the implications of our results on using optical tweezers to collect force-dependent dissociation data.

Nondestructive evaluation of rebar corrosion in concrete structures using ultrasonics and laser-based sensing

ABSTRACT Corrosion is a serious affliction for reinforced concrete (RC) structures and the development of advanced inspection techniques is crucial for maintaining serviceability and durability. In this study, a novel proof-of-concept is presented to monitor corrosion in the concrete by employing contact-based ultrasonic excitation and non-contact laser-based sensing. Accelerated corrosion of rebars in RC slabs is implemented, and ultrasonic scanning is performed on specimens at various corrosion levels. The excitation source is a contact ultrasonic transducer, which enables better penetration of the waves into the concrete medium. For the reception, a Doppler laser vibrometry based sensing is used, which expedites the inspection by circumventing the requirement of coupling. The Synthetic Aperture Focusing Technique (SAFT) is employed for subsurface image reconstruction. Rebar images obtained by hybrid sensing technique, are qualitatively similar to those obtained via traditional contact-based source and receiver system. A histogram-based qualitative analysis is presented to correlate the characteristics of the rebar images to the progress of corrosion. A planar SAFT imaging algorithm is presented to demonstrate the rebar on a horizontal plane for better visualisation. The proposed technique is significantly faster in comparison to the contact-based approach and, therefore, has the potential of being applied for inspection of larger concrete infrastructure.

Using quantitative MRI to study brain responses to immune challenge with interferon-α

Inflammatory processes in the Central Nervous System (CNS) have been proposed to mediate the association between peripheral inflammation and the development of psychiatric disorders, but we currently lack sensitive measures of CNS inflammation for human studies in vivo. Here we used quantitative MRI (qMRI) to explore the in vivo central response to a peripheral immune challenge in healthy humans, and we assessed whether changes in quantitative relaxometry MRI parameters were associated with changes in peripheral inflammation.Quantitative relaxation times (T1 & T2) and Proton Density (PD) were measured in n ​= ​6 healthy males (mean age ​= ​30.5 ​± ​6.8 years) in two MRI assessments collected before and 24 ​hours after a subcutaneous injection of the proinflammatory cytokine and immune activator, interferon-alpha (IFN-α). Serum levels of immune markers and markers of blood-brain barrier integrity (S100B) were also measured before and after the injection.Region of interest and histogram-based analyses (optimized for the small sample size) showed a statistically significant increase of both T1 (t(5) ​= ​3.78, p ​= 0.013) and PD (t(5) ​= ​2.91, p ​= ​0.033) parameters in the bilateral hippocampus after IFN-α administration. T1 peak values in bilateral hippocampus were positively correlated with serum Tumour Necrosis Factor-alpha levels at 24 ​h after the injection, when this cytokine peaked (Spearman's rho ​= ​0.67, p ​= ​0.018) and negatively correlated with S100B levels (Spearman's rho ​= ​−0.826, p ​= ​0.001).Our data suggest that peripheral administration of IFN-α produces acute changes in brain qMRI which might indicate a brain immune response. This is supported by the association of such changes with low-grade peripheral inflammation.

Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer.

Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer. This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2-6mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS). During a median follow-up of 36months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3-5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p < 0.05). Regarding the 2-year RFS for CE-T1 MPP and T2 kurtosis at SSF5, and CE-T1 MPP at SSF6, patients with > optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05). Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.

Diffusion-Weighted Imaging and Mapping of T1 and T2 Relaxation Time for Evaluation of Chronic Renal Allograft Rejection in a Translational Mouse Model.

We hypothesized that multiparametric MRI is able to non-invasively assess, characterize and monitor renal allograft pathology in a translational mouse model of chronic allograft rejection. Chronic rejection was induced by allogenic kidney transplantation (ktx) of BALB/c-kidneys into C57BL/6-mice (n = 23). Animals after isogenic ktx (n = 18) and non-transplanted healthy animals (n = 22) served as controls. MRI sequences (7T) were acquired 3 and 6 weeks after ktx and quantitative T1, T2 and apparent diffusion coefficient (ADC) maps were calculated. In addition, in a subset of animals, histological changes after ktx were evaluated. Chronic rejection was associated with a significant prolongation of T1 time compared to isogenic ktx 3 (1965 ± 53 vs. 1457 ± 52 ms, p < 0.001) and 6 weeks after surgery (1899 ± 79 vs. 1393 ± 51 ms, p < 0.001). While mean T2 times and ADC were not significantly different between allogenic and isogenic kidney grafts, histogram-based analysis of ADC revealed significantly increased tissue heterogeneity in allografts at both time points (standard derivation/entropy/interquartile range, p < 0.05). Correspondingly, histological analysis showed severe inflammation, graft fibrosis and tissue heterogeneity in allogenic but not in isogenic kidney grafts. In conclusion, renal diffusion weighted imaging and mapping of T2 and T1 relaxation times enable detection of chronic renal allograft rejection in mice. The combined quantitative assessment of mean values and histograms provides non-invasive information of chronic changes in renal grafts and allows longitudinal monitoring.

Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images

Background and Objective: Visual expression of invasive breast cancer with immunohistochemistry (IHC) allows evaluation of CerbB2 receptors, such that CerbB2 mutated breast carcinomas are suitable for targeted therapy. Breast tumors are evaluated in four different scores as 0, 1, 2, 3 to decide if it is suitable for the CerbB2 protein specific treatment or not. Pathologists try to decide the scores by eye, which is laborious, and error-prone work with high inter-observer variability.Methods: We propose cell based image analysis termine the CerbB2/HER2 scores in breast tissue images in accordance with ASCO/CAP recommendations, automatically. The proposed ASCO/CAP recommendations compliant image analysis approach provides an explainable artificial intelligence solution for HER2 tissue scoring. Firstly, tissue images are separated into hematoxylin and diaminobenzidine color channels with color deconvolution. Cell nuclei and boundaries are segmented with a hybrid multi-level thresholding and radial line based method on hematoxylin channel. Following ASCO/CAP recommendations, cell based features representing the intensity and completeness of circumferential membrane staining are extracted with the proposed Membrane Intensity Histogram (MIH) method. Extracted features are, then, fed into a classifier, such as, k-nearest neighbours, decision trees and long-short term memory, to determine cell based HER2 scores. Individual cell scores are combined according to ASCO/CAP recommendations to obtain the final CerbB2/HER2 tissue score. Another contribution of the paper is the introduction of two publicly available image data sets on CerbB2/HER2 tissue scoring. Clinical data sets, ITU-MED-1 and ITU-MED-2, are created by digitizing IHC slides from real patients, that have ground truth CerbB2/HER2 scores.Result: The proposed automatic scoring method is tested on these clinical data sets, as well as, on a HER2 Contest data set. Performance of the proposed explainable artificial intelligence approach for HER2 tissue scoring is evaluated and compared with state-of-the-art techniques in the literature.Conclusion: Results suggest that, the proposed method is highly effective in HER2 tissue scoring on both balanced and unbalanced data sets.Significance: A hand-crafted feature extraction approach for CerbB2/HER2 scoring is proposed which provides an explainable artificial intelligence framework. The proposed HER2 scoring method can be adapted to updates in ASCO/CAP recommendations without the need for re-training and/or re-designing the model. Moreover, two publicly available data sets, namely, ITU-MED-1 and ITU-MED-2 are introduced with corresponding score labels.

Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile.

PurposeGlioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.MethodsPre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.ResultsGrade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.ConclusionsADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

Cover independent image steganography in spatial domain using higher order pixel bits

In spatial domain image steganography, Least Significant Bits (LSB) of cover image pixels are used to embed a secret message due to minimal distortion and higher payload capacity. In this paper, we have introduced an exclusive-OR (XOR) based encoding of encrypted secret message bits using varying higher-order pixel intensity bits. Encoding and LSB embedding is done block-wise by dividing the cover image into a number of blocks. The secret message is first encrypted using symmetric key cryptography and then encoded those encrypted bits by XORing them with randomly selected higher-order pixel bis of the cover image to obscure the secret bits further. Next, an inversion technique is applied to the encoded bits block-wise to keep the LSB bit changes to a minimum. The stego-key consists of the symmetric encryption key and the encode-key containing parameter settings such as the number_of_blocks, starting_block, start_pixel_offset, block_selection_rule, etc. This stego-key is shared prior to the actual communication using public-key cryptography to ensure the key’s authenticity and integrity. The extraction process does not require the cover image; the stego-image and the stego-key are sufficient. Experimental results show the visual imperceptibility along with improved image quality metrics such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Structural Similarity (SSIM) index in comparison to other well-known techniques. The average PSNR value remains above 51dB, even with 90% of the capacity utilized. The proposed scheme successfully eludes many standard steganalysis attacks such as histogram-based analysis (PDH), chi-square based embed probability test, Regular and Singular groups (RS) analysis, sample pair test, etc. on the tested stego-images.

Volumetric histogram-based analysis of cardiac magnetic resonance T1 mapping: A tool to evaluate myocardial diffuse fibrosis.

•Cardiac magnetic resonance is a powerful tool to evaluate diffuse fibrosis. •Volumetric histogram-based analysis can precociously detect diffuse fibrosis. •Silent heart diseases would benefit from T1 mapping histogram-based analysis.

Quantitative Evaluation of Fibrosis in IPF Patients: Meaning of Diffuse Pulmonary Ossification.

To investigate the role of diffuse pulmonary ossification (DPO) in disease severity in a population of Idiopathic Pulmonary Fibrosis (IPF) patients. This retrospective study was carried out on 95 IPF patients—44 with DPO on high resolution computed tomography (HRCT) and 51 with no calcifications detected on HRCT. Pulmonary Function Tests (PFTs) acquired nearest to the HRCT were collected. Images were analyzed by two radiologists using a qualitative method, based on HRCT fibrosis visual score, and using a quantitative method, based on histogram-based analysis. The Spearman’s rank correlation coefficient was used to measure the strength and direction of the linear relationship between HRCT fibrosis score and PFTs; in addition, Spearman’s rank correlation coefficient was used to explore the relationships between HRCT fibrosis score and quantitative index and between quantitative indexes and PFTs. A weak correlation between HRCT fibrosis score and PFTs was proven (r =–0.014 and p = 0.9347 for FVC (Forced Vital Capacity), r = −0.379 and p = 0.0174 for DLCO (Carbon monoxide diffusing capacity)). We found a moderate negative correlation between HRCT fibrosis score and kurtosis (r = −0.448, p = 0.004272) and skewness (r = −0.463, p = 0.003019) and a weak positive correlation with High Attenuation Area (HAA)% (r = 0.362, p = 0.0235). Moreover, a moderate linear correlation between Quantitative Indexes and FVC (r = 0.577, p = 0.000051 for kurtosis and FVC, r = 0.598, p = 0.000023 for skewness and FVC, r = −0.519, p = 0.0000364 for HAA% and FVC) and between quantitative indexes and DLCO (r = 0.469, p = 0.001508 for kurtosis, and DLCO, r = 0.474, p = 0.001309 for skewness and DLCO, r = −0.412, p = 0.005996 for HAA% and DLCO) was revealed. To better investigate the influence of DPO in disease progression, a longitudinal evaluation should be performed.

Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers.

We developed multiple histogram-based CBF indices and evaluated their association with histopathologic grade in de novo brain tumor patients. Furthermore, the associations between these advanced CBF indices and molecular markers, including IDH1 mutation, ATRX loss, and 1p/19q co-deletion were also investigated. Thirteen de novo brain tumor patients (age 21-68years, 9M/4F) who were enrolled in our prospective study were scanned on 3T MRI using a pCASL perfusion sequence following IRB-approved written informed consent. All patients have since undergone surgical intervention with tissue sampling for histopathologic tumor grading and molecular marker assessment. Tumor region of interest (ROI) were manually delineated on FLAIR images including the full extent of the tumor and peritumoral edema. Fourteen rCBF indices were derived from the histogram of the voxels with the ROI. Multi-linear regression was then used to compare rCBF indices with histopathologic tumor grade and molecular markers. Averaged rCBF in top 10 and top 20 voxels (p < 0.004), but not the entire tumor ROI, was positively associated with WHO tumor grade. After accounting for tumor grade, the presence of 1p/19q co-deletion was associated with higher rCBF in top voxels, as well as with standard deviation of rCBF in the tumor ROI (p < 0.001). ATRX retention was related to higher rCBF, and this effect appears to be present in both higher-perfusion (p < 0.004) and low-perfusion (p < 0.05) voxels. IDH mutation was not significantly associated with any of the CBF indices investigated. ASL MRI may provide useful supplemental noninvasive imaging assessment of brain tumor grade and molecular marker status.

In-vivo lung fibrosis staging in a bleomycin-mouse model: a new micro-CT guided densitometric approach

Although increasing used in the preclinical testing of new anti-fibrotic drugs, a thorough validation of micro-computed tomography (CT) in pulmonary fibrosis models has not been performed. Moreover, no attempts have been made so far to define density thresholds to discriminate between aeration levels in lung parenchyma. In the present study, a histogram-based analysis was performed in a mouse model of bleomycin (BLM)-induced pulmonary fibrosis by micro-CT, evaluating longitudinal density changes from 7 to 21 days after BLM challenge, a period representing the progression of fibrosis. Two discriminative densitometric indices (i.e. 40th and 70th percentiles) were extracted from Hounsfield Unit density distributions and selected for lung fibrosis staging. The strong correlation with histological findings (rSpearman = 0.76, p < 0.01) confirmed that variations in 70th percentile could reflect a pathological lung condition and estimate the effect of antifibrotic treatments. This index was therefore used to define lung aeration levels in mice distinguishing in hyper-inflated, normo-, hypo- and non-aerated pulmonary compartments. A retrospective analysis performed on a large cohort of mice confirmed the correlation between the proposed preclinical density thresholds and the histological outcomes (rSpearman = 0.6, p < 0.01), strengthening their suitability for tracking disease progression and evaluating antifibrotic drug candidates.

Intravital Imaging of Adoptive T-Cell Morphology, Mobility and Trafficking Following Immune Checkpoint Inhibition in a Mouse Melanoma Model

Efficient T-cell targeting, infiltration and activation within tumors is crucial for successful adoptive T-cell therapy. Intravital microscopy is a powerful tool for the visualization of T-cell behavior within tumors, as well as spatial and temporal heterogeneity in response to immunotherapy. Here we describe an experimental approach for intravital imaging of adoptive T-cell morphology, mobility and trafficking in a skin-flap tumor model, following immune modulation with immune checkpoint inhibitors (ICIs) targeting PD-L1 and CTLA-4. A syngeneic model of ovalbumin and mCherry-expressing amelanotic mouse melanoma was used in conjunction with adoptively transferred OT-1+ cytotoxic T-cells expressing GFP to image antigen-specific live T-cell behavior within the tumor microenvironment. Dynamic image analysis of T-cell motility showed distinct CD8+ T-cell migration patterns and morpho-dynamics within different tumor compartments in response to ICIs: this approach was used to cluster T-cell behavior into four groups based on velocity and meandering index. The results showed that most T-cells within the tumor periphery demonstrated Lévy-like trajectories, consistent with tumor cell searching strategies. T-cells adjacent to tumor cells had reduced velocity and appeared to probe the local environment, consistent with cell-cell interactions. An increased number of T-cells were detected following treatment, traveling at lower mean velocities than controls, and demonstrating reduced displacement consistent with target engagement. Histogram-based analysis of immunofluorescent images from harvested tumors showed that in the ICI-treated mice there was a higher density of CD31+ vessels compared to untreated controls and a greater infiltration of T-cells towards the tumor core, consistent with increased cellular trafficking post-treatment.

Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images

The majority of the change detection (CD) methods consider spatial information by using a regular window or strict mathematical model. Moreover, these methods use the spectra directly to measure the change magnitude between bitemporal images. To solve this problem, local histogram-based analysis (LHBA) is proposed for detecting a land cover change in this letter. This new approach aims to inhibit the pseudo change by defining the local histogram trend (LHT) in an adaptive manner instead of using spectral values to measure change magnitude directly. In the proposed approach, the spatial information around each pixel is first exploited by defining an adaptive local histogram. The LHT distance between the pairwise local histograms is then developed to measure the change magnitude between the pairwise pixels of bitemporal images. Finally, the change magnitude image is generated, and a binary CD is achieved by a threshold method. Experiments based on two pairs of very high-resolution remote sensing images, which refer to land use change and landslides events, demonstrate the advantages and performance of the proposed approach.

Robust identification of QRS-complexes in electrocardiogram signals using a combination of interval and trigonometric threshold values

Combination of interval and amplitude threshold-based algorithms have been tested rigorously over the last few decades for detecting the QRS-complexes in electrocardiogram (ECG) signals. However, the very eccentric nature of appearance of the QRS-complexes often make an amplitude threshold-based algorithm fail. This paper presents a unique combination of trigonometric feature and interval threshold-based algorithm for efficient detection of the QRS-complexes. First, the ECG signal is denoised, and the first-difference (FD) of the denoised ECG is calculated. The FD signal is then sequentially passed through a Hilbert and Shannon energy (SE) transformers. Next, a histogram-based analysis technique is applied on the SE-transformed data to filter out the contributions of the low frequency components of the ECG signal. Next, each of the modified SE-transformed data having a non-zero amplitude value is mapped onto the denoised ECG signal, and a unique trigonometric feature is extracted from that corresponding location of the denoised ECG signal. Finally, the QRS-complexes are identified using a combination of trigonometric and interval-threshold values. Wired and wireless ECG signals are collected from seven databases, and are used as the evaluation test-beds of the proposed algorithm. The performance of the algorithm is found highly-competent compared to that of the state-of-the-art methods. The significance of the proposed algorithm is that, not only the detection-accuracy of the algorithm is high, it is also fast; the trigonometric feature can be used to extract the breathing rate and pattern; the trigonometric feature can also be used for the identification of the abnormal QRS-complexes.