Multiple Regions Of Interest Research Articles

Reconnaissance of tumor immune microenvironment spatial heterogeneity in metastatic renal cell carcinoma and correlation with immunotherapy response.

A clearer understanding of the tumor immune microenvironment (TIME) in metastatic clear cell renal cell carcinoma (ccRCC) may help to inform precision treatment strategies. We sought to identify clinically meaningful TIME signatures in ccRCC. We studied tumors from 39 patients with metastatic ccRCC using quantitative multiplexed immunofluorescence and relevant immune marker panels. Cell densities were analyzed in three regions of interest (ROIs): tumor core, tumor-stroma interface and stroma. Patients were stratified into low- and high-marker density groups using median values as thresholds. Log-rank and Cox regression analyses while controlling for clinical variables were used to compare survival outcomes to patterns of immune cell distributions. There were significant associations with increased macrophage (CD68+ CD163+ CD206+ ) density and poor outcomes across multiple ROIs in primary and metastatic tumors. In primary tumors, T-bet+ T helper type 1 (Th1) cell density was highest at the tumor-stromal interface (P=0·0021), and increased co-expression of CD3 and T-bet was associated with improved overall survival (P=0·015) and survival after immunotherapy (P=0·014). In metastatic tumor samples, decreased forkhead box protein 3 (FoxP3)+ T regulatory cell density correlated with improved survival after immunotherapy (P=0·016). Increased macrophage markers and decreased Th1 T cell markers within the TIME correlated with poor overall survival and treatment outcomes. Immune markers such as FoxP3 showed consistent levels across the TIME, whereas others, such as T-bet, demonstrated significant variance across the distinct ROIs. These findings suggest that TIME profiling outside the tumor core may identify clinically relevant associations for patients with metastatic ccRCC.

Comparing lifeact and phalloidin for super-resolution imaging of actin in fixed cells.

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide ‘lifeact’. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a dSTORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.

An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm

It is the most challenging and difficult task to segment a satellite image because of its complete randomness, multiple regions of interest, weak correlation with pixels, and regions of ambiguity. There are several Nature-inspired algorithms available, which are used to overcome these difficulties and those are more efficient to generate the best threshold value for the segmentation of satellite images. Though various modern methodologies opt for better results but methods have some drawbacks too like techniques are computationally expensive and time-consuming. In this paper, we have proposed a more effective satellite image segmentation approach using a new adaptive cuckoo search (ACS) algorithm. The result obtained from the projected technique is compared with CSMcCulloch incorporating McCulloch’s method for levy flight generation in Cuckoo Search (CS) algorithm by using two different objective functions namely Otsu’s method and Tsallis entropy function. The measurement techniques such as PSNR, MSE, FSIM, SSIM, UIQI, and computational time in term of CPU running time have been considered for validating and evaluating the proposed method. This proposed algorithm technique has resulted in improve segmentation quality of satellite images and reduced computational time. The analysis of the convergence rate proves that ACS is superior to the CSMcCulloch algorithm for reaching the global convergence rate. These experimental outcomes help to encourage researchers in different domains such as computer vision, application of medical image analysis, machine learning as well as deep learning.

Assessment of Blood-Brain Barrier Integrity in Tuberculous Meningitis Using Dynamic Contrast-Enhanced MR Perfusion.

Objective Tuberculous meningitis (TBM) is the most common form of central nervous system tuberculosis. The aim of the study was to quantitatively evaluate blood–brain barrier (BBB) perfusion changes in TBM patients using dynamic contrast-enhanced (DCE) MR perfusion. Methods and Material Thirty untreated patients of TBM and 10 healthy controls were prospectively evaluated by conventional imaging and DCE MR perfusion. Mean permeability indices— K trans and Ve—were calculated from multiple regions of interest (ROIs) placed in basal cisterns and comparison was done between the patients and controls. Results The permeability indices were significantly higher (where p < 0.001) in cisterns of TBM patients who showed basal meningeal enhancement when compared with healthy controls. Significant differences in permeability were observed between “enhancing” cases and controls as well as in “enhancing” cases when compared with the “non-enhancing” cases. However, no significant difference was observed in the mean cisternal value between “non-enhancing” cases and the controls. K trans with a cutoff value of > 0.0838 had 81.6% sensitivity and 78.6% specificity in differentiating cases and controls while V e mean value with a cutoff value of 0.0703 showed 86.8% sensitivity and 91.4% specificity in predicting the permeability difference between the cases and controls. Conclusion DCE MR perfusion is useful in the quantitative measurement of disruption of BBB and perfusion alterations in patients of TBM.

Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects.

Functional renal MRI promises access to a wide range of physiologically relevant parameters such as blood oxygenation, perfusion, tissue microstructure, pH, and sodium concentration. For quantitative comparison of results, representative values must be extracted from the parametric maps obtained with these different MRI techniques. To improve reproducibility of results this should be done based on regions-of-interest (ROIs) that are clearly and objectively defined.Semiautomated subsegmentation of the kidney in magnetic resonance images represents a simple but very valuable approach for the quantitative analysis of imaging parameters in multiple ROIs that are associated with specific anatomic locations. Thereby, it facilitates comparing MR parameters between different kidney regions, as well as tracking changes over time.Here we provide detailed step-by-step instructions for two recently developed subsegmentation techniques that are suitable for kidneys of small rodents: i) the placement of ROIs in cortex, outer and the inner medulla based on typical kidney morphology and ii) the division of the kidney into concentrically oriented layers.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.

Fast fluorescence lifetime microscopy imaging of any number of discrete irregular regions of interest

Fluorescence lifetime imaging microscopy (FLIM) has been widely used in biomedical research due to its high specificity, high sensitivity and quantification ability in cell microenvironment sensing. The fluorescence lifetime detection method based on time-correlated single photon counting (TCSPC) is one of the most commonly used techniques at present. However, due to the limitation of imaging principles and conditions, this technique has the disadvantages of long data acquisition time and consequently low imaging speed. In this paper, a fast FLIM technique for any number of discrete and irregular regions of interest (ROIs) in biological samples is developed. The technology uses acousto-optic deflectors (AODs) to achieve fast and flexible addressing scanning, optimize the synchronization strategy between AOD and TCSPC, and reconstruct the lifetime image through simple online feature analysis of the ROI shapes. For the case of multiple discrete irregular ROIs in biological samples, it can greatly save the time of data acquisition, thus realizing the fast FLIM imaging of these ROIs, which is benificial to the study of the heterogeneity of biological events in biological system. In particular, the fast fluorescence imaging result for 87 discrete points in the field of view shows that this method can obtain a fluorescence lifetime image in a very short acquisition time (only 52.2 ms) and thus achieving a very fast imaging speed in such a situation. Dynamic FLIM imaging of lysosome probe LysoSensor Green DND-189 in living cells stimulated by ammonium chloride is carried out to monitor the real-time change of pH value in lysosome lumen. The acquisition time for a single fluorescence lifetime image of lysosomes in two ROIs is only 200 ms. The results show that the rapid FLIM technology can be used to dynamically monitor the changes of microenvironment in biological samples, and will play an important role in the microenvironment sensing in living cells.

Fast Foveating Cameras for Dense Adaptive Resolution.

Traditional cameras field of view (FOV) and resolution predetermine computer vision algorithm performance. These trade-offs decide the range and performance in computer vision algorithms. We present a novel foveating camera whose viewpoint is dynamically modulated by a programmable micro-electromechanical (MEMS) mirror, resulting in a natively high-angular resolution wide-FOV camera capable of densely and simultaneously imaging multiple regions of interest in a scene. We present calibrations, novel MEMS control algorithms, a real-time prototype, and comparisons in remote eye-tracking performance against a traditional smartphone, where high-angular resolution and wide-FOV are necessary, but traditionally unavailable.

Adaptive sampling with an autonomous underwater vehicle in static marine environments

AbstractThis paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point‐source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real‐time, for which standard (e.g., grid‐based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions. Specifically, this study employs the GP upper confidence bound as the optimization criteria to adaptively plan sampling paths that balance a trade‐off between exploration and exploitation. Two informative path planning algorithms based on (i) branch‐and‐bound techniques and (ii) cross‐entropy optimization are presented for choosing future sampling locations while considering the motion constraints of the sampling platform. The effectiveness of the proposed methods are explored in simulated scalar fields for identifying multiple regions of interest within a three‐dimensional environment. Field experiments with an AUV using both virtual measurements on a known scalar field and in situ dissolved oxygen measurements for studying hypoxic zones validate the approach's capability to quickly explore the given area, and then subsequently increase the sampling density around regions of interest without sacrificing model fidelity of the full sampling area.

Differential protein expression identified by digital spatial profiling (DSP) in the hippocampi of resilient individuals

AbstractBackgroundAn intriguing group of individuals, termed “resilient”, demonstrate neuropathologic changes consistent with Alzheimer disease (AD), yet display no cognitive impairment. Studies have reported less neuronal loss overall, less gliosis, and fewer comorbidities (such as cerebrovascular disease) in these individuals. We sought to analyze protein expression in and around normal and neurofibrillary tangle (NFT)‐bearing neurons in these individuals as compared to those with cognitive decline.MethodUtilizing Nanostring’s GeoMx™ Digital Spatial Profiling (DSP) technology, we compared individuals with AD neuropathologic change and dementia (n = 6) to individuals with AD neuropathologic change and no dementia (n = 8). The DSP panel allowed for spatial analysis of the level of expression of 89 different proteins in multiple regions of interest on formalin‐fixed paraffin embedded sections. We analyzed hippocampal sections, with our regions of interest (ROIs) being NFT‐bearing neurons, non‐NFT‐bearing neurons, and their immediate neuronal microenvironments (50 μm diameter surrounding the neuronal cell body) (Figure 1).ResultAnalyses identified 11 proteins having differential expression in tangle‐bearing neurons when comparing resilient to demented individuals. These included IDH1, GFAP, CD68, IDE, SYP and Histone H3. In addition, IDH1, CD68 and SYP were differentially expressed in the environment of normal and tangle‐bearing neurons when comparing resilient to demented individuals. IDH1 displayed significantly lower expression in the resilient individuals. It is thought that IDH1 is upregulated under energetic and oxidative stress. Similarly, PINK1 (which is upregulated in response to mitochondrial dysfunction and oxidative stress) displayed lower expression in the environment of normal and tangle‐bearing neurons in the resilient. Therefore, we hypothesize that there is less energetic and oxidative stress in the resilient individuals. Synaptophysin (SYP) is increased in the resilient, which likely indicates better maintenance of neuronal synapses and synaptic connections. Furthermore, neurofilament light chain (NEFL) and ubiquitin c‐terminal hydrolase (Park5) expression were higher in the resilient compared to the demented in the environment of NFTs. These differences all suggest healthier intact axons, dendrites and synapses in the resilient.ConclusionIn conclusion, resilient individuals display protein expression patterns suggestive of an environment containing less energetic and oxidative stress, which in turn results in maintenance of neurons and their synaptic connections.

Auto-ROIs of bone scan image using thin-plate spline with specific bone landmarks

Bone scan image (BSI) is the most widely available technique for detecting cancer that has spread (metastasized) from the original location, such as breast or prostate cancer. One benefit of this process is as strong prognostic indicator of survival longevity for cancer patient. However, some challenging issues should be considered, i.e. variability in image brightness, abnormal bone shape or structure, asymmetry gesture position, and image noise due to muscle and fat tissue make the process to determine the region of interest (ROI) become more difficult, not to mention the required-time when inspecting the ROI of each bone part manually. The purposes of this study are to provide an effortless and meticulous ROI labeling as prerequired in clinical application. Finally, to propose a novel framework for determination of multiple ROI automatically by utilizing specific bone landmarks, in the hope of that could contribute to clinical observation in metastasis analysis. The results show that the proposed framework is promising method with high sensitivity (0.96), specificity (0.97), precision (0.94), Sorensen-Dice Index (0.96) and image content index (0.96).

A semi-automated brain atlas-based analysis pipeline for c-Fos immunohistochemical data

BackgroundThe use of immunohistochemistry to quantify neural markers in various brain regions is a staple of neuroscience research. Numerous programs exist to automate quantification, but manual assignment of regions of interest (ROIs) within individual brain sections remains time consuming and can introduce interobserver variability. New methodWe have developed a novel open source FIJI-based immunohistochemical data analysis pipeline, Atlas-Based Analysis (ABA). ABA uses landmark-based image warping to adjust the experimental image to closely align with a published rat brain atlas. c-Fos positive cells are then quantified within predetermined ROI coordinates derived from the brain atlas. Image warping adjusts for natural variation in brain sections to ensure reliable alignment of ROIs for data analysis. This pipeline can be adapted for new atlases, landmarks, ROIs, and quantification measurements. ResultsABA permits rapid quantification of immunoreactivity in multiple ROIs and produces results with high levels of interobserver consistency. Comparison with existing methodsCompared to manual ROI designation, ABA reduces total analysis time by ∼70%. With correct use of landmarks for image warping, ABA produces similar results to manually drawn ROIs, results in no interobserver variability, and maintains c-Fos+ pixel dimensions. ConclusionsABA reduces time to obtain reliable results when performing automated immunoreactivity quantification and allows multiple users to analyze data without compromising the reliability of data obtained.

Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach.

BackgroundBone age assessment (BAA) is used in numerous pediatric clinical settings as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical as the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods have drawbacks such as exposure of minors to radiation, they do not consider factors that might affect the bone age, and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals, it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA.ObjectiveThis study aims to investigate CA estimation through BAA in young individuals aged 14-21 years with machine learning methods, addressing the drawbacks of research using magnetic resonance imaging (MRI), assessment of multiple regions of interest, and other factors that may affect the bone age.MethodsMRI examinations of the radius, distal tibia, proximal tibia, distal femur, and calcaneus were performed on 465 men and 473 women (aged 14-21 years). Measures of weight and height were taken from the subjects, and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, and type of residence during upbringing). Two pediatric radiologists independently assessed the MRI images to evaluate their stage of bone development (blinded to age, gender, and each other). All the gathered information was used in training machine learning models for CA estimation and minor versus adult classification (threshold of 18 years). Different machine learning methods were investigated.ResultsThe minor versus adult classification produced accuracies of 0.90 and 0.84 for male and female subjects, respectively, with high recalls for the classification of minors. The CA estimation for the 8 age groups (aged 14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter, a lower error occurred only for the ages of 14 and 15 years.ConclusionsThis study investigates CA estimation through BAA using machine learning methods in 2 ways: minor versus adult classification and CA estimation in 8 age groups (aged 14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results; however, for the second case, the BAA was not precise enough for the classification.

Thermal Sensation in Older People with and without Dementia Living in Residential Care: New Assessment Approaches to Thermal Comfort Using Infrared Thermography.

The temperature of the indoor environment is important for health and wellbeing, especially at the extremes of age. The study aim was to understand the relationship between self-reported thermal sensation and extremity skin temperature in care home residents with and without dementia. The Abbreviated Mental Test (AMT) was used to discriminate residents to two categories, those with, and those without, dementia. After residents settled and further explanation of the study given (approximately 15 min), measurements included: tympanic membrane temperature, thermal sensation rating and infrared thermal mapping of non-dominant hand and forearm. Sixty-nine afebrile adults (60–101 years of age) were studied in groups of two to five, in mean ambient temperatures of 21.4–26.6 °C (median 23.6 °C). Significant differences were observed between groups; thermal sensation rating (p = 0.02), tympanic temperature (p = 0.01), fingertip skin temperature (p = 0.01) and temperature gradients; fingertip-wrist p = 0.001 and fingertip-distal forearm, p = 0.001. Residents with dementia were in significantly lower air temperatures (p = 0.001). Although equal numbers of residents per group rated the environment as ‘neutral’ (comfortable), resident ratings for ‘cool/cold’ were more frequent amongst those with dementia compared with no dementia. In parallel, extremity (hand) thermograms revealed visual temperature demarcation, variously across fingertip, wrist, and forearm commensurate with peripheral vasoconstriction. Infrared thermography provided a quantitative and qualitative method to measure and observe hand skin temperature across multiple regions of interest alongside thermal sensation self-report. As an imaging modality, infrared thermography has potential as an additional assessment technology with clinical utility to identify vulnerable residents who may be unable to communicate verbally, or reliably, their satisfaction with indoor environmental conditions.

Abstract 310: Immunophenotyping tumors by novel multiplex immunofluorescence and AQUA (Automated Quantitative Analyses) algorithms to guide development of immunotherapies

Abstract Purpose: Deeper understanding of immune landscape of the tumor microenvironment is critical for exploring and development of next generation immunotherapies. Multiplex fluorescence immunohistochemistry (mFIHC) combined with hypothesis driven spatial profiling algorithms (e.g., AQUA Technology) was found to provide the most powerful predictors of immunotherapies in a systematic meta-analyses of over 8000 patients treated with PD1/L1 pathway blockers (Lu et al., JAMA Oncol 2019). Study Design: To guide clinical development of next generation and/or combination immunotherapies, we built four novel mFIHC assays to simultaneously explore presence of major immune cell lineages, their spatial relationships and functional attributes. The first assay incorporates antibodies to identify major immune cells lineages (e.g., T-, B-, NK-/T- and subtypes of myeloid cells), the second assay is designed to understand T-cell proliferation (via co-expression of Ki67) or suppression (via co-expression of FoxP3), the third method is focused on quantifying the expression of immune checkpoint inhibitors (e.g., LAG3, PD1 and PD-L1), while, the final assay is designed to explore the IFNγ mediated adaptive resistance and immunosuppression (via co-expression of HLA-DR and IDO1). We will describe the successful development of these clinical grade mFIHC assays that utilize automated staining (Leica Bond RX), imaging (Vectra Polaris) and analyses (AQUA® Technology) workflows on tissue microarrays (TMAs) representing multiple regions of interest including tumor, tumor margin, tumor-associated fibrosis, various adjacent normal tissues, lymphoid structures and/or associated inflammation, and metastatic lesions collected from 100 colorectal, and pancreatic cancer patients. Results: Sensitivity, accuracy and specificity were confirmed for all mFIHC assays on known positive and negative controls. Excellent reproducibility (less than 35% CV) and precision were observed across instruments, operators and independent experiments for all markers. TMA work is underway and the latest results will be presented. Conclusion: The validated mFIHC assays helped classify unique immune phenotypes and their interactions. These assays, combined with comprehensive selection of tissues from the tumor microenvironment across multiple cancer subtypes, are expected to enable clinical development of next generation cancer therapies. Citation Format: Ju Young Kim, John Hoerter, Nathan Roscoe, John Fathman, James Santos, Lori Iaconis, Justin Santos, Minhua Qiu, Jacob Levy, Valerie Tolley, Emmanuel Pacia, Tom Carolan, Xun Li, Patrizia Barzaghi-Rinaudo, Brian Schwartz, Robbin Newlin, Karyn Colman, Andy Schumacher, Deborah Knee, Steven Bender, Margaret McLaughlin, Alexander Savchenko, Thai Tran, Shabnam Tangri, Naveen Dakappagari, Jennifer Bordeaux. Immunophenotyping tumors by novel multiplex immunofluorescence and AQUA (Automated Quantitative Analyses) algorithms to guide development of immunotherapies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 310.

Comparison of different automatic methods for the delineation of the total metabolic tumor volume in I\u2013II stage Hodgkin Lymphoma

Total metabolic tumor volume (TMTV) is a promising quantitative biomarker for therapy assessment and prognosis in Hodgkin Lymphoma affected patients that allows prediction of patient outcome. The aim of this study was to evaluate the TMTV reproducibility between different sources of variability in tumor delimitation such as SUV-based thresholds (2.5, 41% and 50%) and software tools (Beth Israel plugin (BI) and LIFEx). Effect of contouring procedure both including single and multiple regions of interest was also studied in patients with multiple lesions, and optimal cut-offs for each studied method were displayed to compare the effect on prognosis. Strong alikeness in TMTV was found for 2.5 under software choice. Best accuracy in contouring compared to visual assessment of the disease was found for BI multiple ROI and LIFEx single ROI drawing. Similar cut-offs were found between both software for all considered thresholds, but best resemblance and highest cut-off due to an overestimation of the TMTV was found for 2.5 SUV. Our findings suggest that optimal reproducibility in TMTV is found for SUV > 2.5 threshold under choice of contouring methodology or software tool, meaning that overestimation of the TMTV threshold using 2.5 looks to be preferable than underestimation with 41% and 50%.

METROID: an automated method for robust quantification of subcellular fluorescence events at low SNR

BackgroundIn cell biology, increasing focus has been directed to fast events at subcellular space with the advent of fluorescent probes. As an example, voltage sensitive dyes (VSD) have been used to measure membrane potentials. Yet, even the most recently developed genetically encoded voltage sensors have demanded exhausting signal averaging through repeated experiments to quantify action potentials (AP). This analysis may be further hampered in subcellular signals defined by small regions of interest (ROI), where signal-to-noise ratio (SNR) may fall substantially. Signal processing techniques like blind source separation (BSS) are designed to separate a multichannel mixture of signals into uncorrelated or independent sources, whose potential to separate ROI signal from noise has been poorly explored. Our aims are to develop a method capable of retrieving subcellular events with minimal a priori information from noisy cell fluorescence images and to provide it as a computational tool to be readily employed by the scientific community.ResultsIn this paper, we have developed METROID (Morphological Extraction of Transmembrane potential from Regions Of Interest Device), a new computational tool to filter fluorescence signals from multiple ROIs, whose code and graphical interface are freely available. In this tool, we developed a new ROI definition procedure to automatically generate similar-area ROIs that follow cell shape. In addition, simulations and real data analysis were performed to recover AP and electroporation signals contaminated by noise by means of four types of BSS: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and two versions with discrete wavelet transform (DWT). All these strategies allowed for signal extraction at low SNR (− 10 dB) without apparent signal distortion.ConclusionsWe demonstrate the great capability of our method to filter subcellular signals from noisy fluorescence images in a single trial, avoiding repeated experiments. We provide this novel biomedical application with a graphical user interface at https://doi.org/10.6084/m9.figshare.11344046.v1, and its code and datasets are available in GitHub at https://github.com/zoccoler/metroid.

Effect of Plate Type and Working Length on a Synthetic Compressed Juxta-Articular Fracture Model

Abstract Objective This investigation compared the biomechanical properties of a 2.0 mm locking compression notched head T-plate (NHTP) and 2.0 mm straight locking compression plate (LCP), in a compressed, short, juxta-articular fragment fracture model. Methods Two different screw configurations were compared for the NHTP and LCP, modelling short (configuration 1) and long working length (configuration 2). Constructs were tested in compression, perpendicular and tension four-point bending and torsion. Plate surface strain was measured at 12 regions of interest using three-dimensional digital image correlation. Stiffness and strain were compared. Results The LCP was stiffer than the NHTP in all three planes of bending (p &lt; 0.05). The NHTP was stiffer than the LCP in torsion (p &lt; 0.05). The NHTP had greater strain than the LCP during compression bending and torsion (p &lt; 0.0005). The short working length NHTP was stiffer in all three planes of bending and in torsion (p &lt; 0.05) than the longer working length. The short working length LCP was stiffer in compression bending and in torsion (p &lt; 0.05) than the longer working length. The long working length showed greater strain than the short working length at multiple regions of interest. Conclusion In this experimental model of a compressed transverse fracture with a juxta-articular 9 mm fragment, a 2.0 mm LCP with two hybrid screws in the short fragment was stiffer than a 2.0 mm NHTP with three locking screws in the short fragment in three planes of bending but not torsion. Extending the working length of each construct reduced construct stiffness and increased plate strain.

Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps

Classical image segmentation algorithms exploit the detection of similarities and discontinuities of different visual cues to define and differentiate multiple regions of interest in images. However, due to the high variability and uncertainty of image data, producing accurate results is difficult. In other words, segmentation based just on color is often insufficient for a large percentage of real-life scenes. This work presents a novel multi-modal segmentation strategy that integrates depth and appearance cues from RGB-D images by building a hierarchical region-based representation, i.e., a multi-modal segmentation tree (MM-tree). For this purpose, RGB-D image pairs are represented in a complementary fashion by different segmentation maps. Based on color images, a color segmentation tree (C-tree) is created to obtain segmented and over-segmented maps. From depth images, two independent segmentation maps are derived by computing planar and 3D edge primitives. Then, an iterative region merging process can be used to locally group the previously obtained maps into the MM-tree. Finally, the top emerging MM-tree level coherently integrates the available information from depth and appearance maps. The experiments were conducted using the NYU-Depth V2 RGB-D dataset, which demonstrated the competitive results of our strategy compared to state-of-the-art segmentation methods. Specifically, using test images, our method reached average scores of 0.56 in Segmentation Covering and 2.13 in Variation of Information.

Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.

The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.

Impact of respiratory motion on liver stiffness measurements according to different shear wave elastography techniques and region of interest methods: a phantom study.

PurposeThis study quantified the impact of respiratory motion on liver stiffness measurements according to different shear wave elastography (SWE) techniques and region of interest (ROI) methods, using liver fibrosis phantoms.MethodsThree operators measured stiffness values in four phantoms with different stiffness on a moving platform with two SWE techniques (point-SWE [pSWE] and 2-dimensional SWE [2D-SWE]), three types of motion (static mode and moving mode at low and high speeds), and four ROI methods in 2D-SWE (circle, point, box, and multiple). The circular ROI method was used to compare the two SWE techniques. The occurrence of technical failure and unreliable measurements, stiffness values, and measurement time were evaluated.ResultsTechnical failure was observed only in moving mode for pSWE and 2D-SWE (n=1 for both). Unreliable measurements were also only observed in moving mode and were significantly less common in 2D-SWE (n=1) than in pSWE (n=12) (P<0.001). No statistically significant differences in the technical failure rate or stiffness values were noted between the static and moving modes for both SWE techniques. The technical failure and unreliable measurement rates were not significantly different among the ROI methods for 2D-SWE. Stiffness values did not differ significantly according to the ROI method used in any moving mode. However, the multiple ROI method had significantly shorter measurement times than the circular ROI method for all moving modes.Conclusion2D-SWE may be preferable for evaluating liver fibrosis in patients with poor breath-hold. Furthermore, 2D-SWE with multiple ROIs enables rapid measurements, without affecting liver stiffness values.