Image Analysis Software Package Research Articles

ColocZStats: a z-stack signal colocalization extension tool for 3D slicer.

Confocal microscopy has evolved to be a widely adopted imaging technique in molecular biology and is frequently utilized to achieve accurate subcellular localization of proteins. Applying colocalization analysis on image z-stacks obtained from confocal fluorescence microscopes is a dependable method of revealing the relationship between different molecules. In addition, despite the established advantages and growing adoption of 3D visualization software in various microscopy research domains, there have been few systems that can support colocalization analysis within a user-specified region of interest (ROI). In this context, several broadly employed biological image visualization platforms are meticulously explored in this study to understand the current landscape. It has been observed that while these applications can generate three-dimensional (3D) reconstructions for z-stacks, and in some cases transfer them into an immersive virtual reality (VR) scene, there is still little support for performing quantitative colocalization analysis on such images based on a user-defined ROI and thresholding levels. To address these issues, an extension called ColocZStats (pronounced Coloc-Zee-Stats) has been developed for 3D Slicer, a widely used free and open-source software package for image analysis and scientific visualization. With a custom-designed user-friendly interface, ColocZStats allows investigators to conduct intensity thresholding and ROI selection on imported 3D image stacks. It can deliver several essential colocalization metrics for structures of interest and produce reports in the form of diagrams and spreadsheets.

Step height measurement of monoatomic silicon crystal lattice steps with a commercial atomic force microscope

Abstract Precise control of advanced materials relies on accurate dimensional metrology at the sub-nanometre scale. At this scale, the accuracy of scanning probe microscopy (SPM) has been limited by the lack of traceable transfer standard artefacts with calibration structures of suitable dimensions. With the adoption in 2019 of the silicon crystal lattice spacing as a secondary realization of the metre in the International System of Units (SI), SPM users have direct access to a realization of the SI metre at the sub-nanometre level by means of the step height of self-assembled monatomic lattice steps that can form on the surface of silicon crystals. A key challenge of successfully adopting this pathway is establishing the need for established protocols to minimize measurement errors and artifacts in routine laboratory use.
In this study, step height measurements of monoatomic lattice steps in an ordinal/staircase structure on a Si(111) crystal surface have been derived from images acquired with a commercially available, research-level atomic force microscope (AFM). Measurement results derived from AFM images using three different SPM image processing and analysis software packages are compared. Significant sources of measurement uncertainty are identified; principally the contribution from the dependence on scan direction. The calibration of the
AFM derived from this measurment was used to traceably measure the sub-nanometre lattice steps on a silicon carbide crystal surface to demonstrate the viability of this calibration pathway.

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model

BackgroundMachine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth. MethodsPatients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. ResultsOf 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, −6.82 to 35.1) and zone 5 (13.4%; IQR, −7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). ConclusionsWe describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

KairoSight-3.0: A validated optical mapping software to characterize cardiac electrophysiology, excitation-contraction coupling, and alternans.

Cardiac optical mapping is an imaging technique that measures fluorescent signals across a cardiac preparation. Dual optical imaging of voltage-sensitive and calcium-sensitive probes allows for simultaneous recordings of cardiac action potentials and intracellular calcium transients with high spatiotemporal resolution. The analysis of these complex optical datasets is both time intensive and technically challenging; as such, we have developed a software package for semi-automated image processing and analysis. Herein, we report an updated version of our software package (KairoSight-3.0) with features to enhance the characterization of cardiac parameters using optical signals. To test software validity and applicability, we used Langendorff-perfused heart preparations to record transmembrane voltage and intracellular calcium signals from the epicardial surface. Isolated hearts from guinea pigs and rats were loaded with a potentiometric dye (RH237) and/or calcium indicator dye (Rhod-2AM) and fluorescent signals were acquired. We used Python 3.8.5 programming language to develop the KairoSight-3.0 software. Cardiac maps were validated with a user-specified manual mapping approach. Manual maps of action potential duration (30 or 80 % repolarization), calcium transient duration (30 or 80 % reuptake), action potential and calcium transient alternans were constituted to validate the accuracy of software-generated maps. Manual and software maps had high accuracy, with >97 % of manual and software values falling within 10 ms of each other and >75 % within 5 ms for action potential duration and calcium transient duration measurements (n = 1000-2000 pixels). Further, our software package includes additional measurement tools to analyze signal-to-noise ratio, conduction velocity, action potential and calcium transient alternans, and action potential-calcium transient coupling time to produce physiologically meaningful optical maps. KairoSight-3.0 has enhanced capabilities to perform measurements of cardiac electrophysiology, calcium handling, alternans, and the excitation-contraction coupling with satisfactory accuracy.

Multimodal ultrasound muscle assessment in patients with rheumatic diseases: a patient-based reliability study.

The aim was to explore the inter-reliability of a newly developed US scanning protocol (multimodal US) for the assessment of different aspects of sarcopenia-related muscle involvement, including muscle mass, muscle quality and muscle stiffness [using point shear-wave elastography (SWE)], in patients with rheumatic and musculoskeletal diseases (RMDs). Quadriceps muscle mass (i.e. muscle thickness), muscle quality (i.e. muscle echogenicity evaluated with both a visual semi-quantitative scale and a dedicated software package for image analysis, ImageJ) and point SWE measurements were obtained by two rheumatologists (blinded to each other's evaluation) in consecutive RMD patients without previous/current myositis or neuromuscular disorders.Inter-reliability was assessed using the intraclass correlation coefficient (ICC) for continuous variables and Cohen's kappa (κ) for categorical variables. A total of 45 RMD patients were enrolled [mean age 54.5 (16.0) years, male-to-female ratio 1:1.5, mean BMI 24.6 (4.6) kg/m2], 10 with PsA, 7 RA, 5 AS, 5 PMR, 4 SLE, 4 gout, 4 OA, 3 FM and 3 SSc. The grade of inter-rater reliability was excellent for muscle mass [ICC = 0.969 (0.953 < ICC < 0.979)]. Regarding muscle echogenicity, the agreement was substantial/almost perfect using the visual semi-quantitative scale (weighted linear = 0.793, weighted squared = 0.878) and excellent using ImageJ analysis [ICC = 0.916 (0.876 < ICC < 0.944)]. Finally, a good agreement was obtained for point SWE measurements [ICC = 0.76 (0.712 < ICC < 0.8)]. Multimodal US is a novel and reliable tool for the evaluation of different aspects of muscle involvement (muscle mass, muscle quality and muscle stiffness) in RMD patients.

Correspondence between cognitive and neural representations for phonology, orthography, and semantics in supramarginal compared to angular gyrus.

The angular and supramarginal gyri (AG and SMG) together constitute the inferior parietal lobule (IPL) and have been associated with cognitive functions that support reading. How those functions are distributed across the AG and SMG is a matter of debate, the resolution of which is hampered by inconsistencies across stereotactic atlases provided by the major brain image analysis software packages. Schematic results from automated meta-analyses suggest primarily semantic (word meaning) processing in the left AG, with more spatial overlap among phonological (auditory word form), orthographic (visual word form), and semantic processing in the left SMG. To systematically test for correspondence between patterns of neural activation and phonological, orthographic, and semantic representations, we re-analyze a functional magnetic resonance imaging data set of participants reading aloud 465 words. Using representational similarity analysis, we test the hypothesis that within cytoarchitecture-defined subregions of the IPL, phonological representations are primarily associated with the SMG, while semantic representations are primarily associated with the AG. To the extent that orthographic representations can be de-correlated from phonological representations, they will be associated with cortex peripheral to the IPL, such as the intraparietal sulcus. Results largely confirmed these hypotheses, with some nuanced exceptions, which we discuss in terms of neurally inspired computational cognitive models of reading that learn mappings among distributed representations for orthography, phonology, and semantics. De-correlating constituent representations making up complex cognitive processes, such as reading, by careful selection of stimuli, representational formats, and analysis techniques, are promising approaches for bringing additional clarity to brain structure-function relationships.

An integrated strategy for chemical, biological and palynological standardization of bee propolis

An integrated method for standardization of propolis using chromatographic, spectroscopic, palynological and biological analyses was implemented in this study. Digitally-enhanced HPTLC images using dual visualization approach with four image analysis software packages adopting different digitalization algorithms were comparatively evaluated i.e.; Sorbfil TLC View®, ImageJ®, JustTLC® and Gel Analyzer®. ImageJ® and Gel Analyzer® showed superior figures of merits in case of flavonoids and phenolic acids quantitation, respectively. Unsupervised and supervised multivariable pattern recognition models were constructed for comprehensive discrimination between the three propolis types (blue, orange and green) collected from various geographical locations worldwide. HPTLC-ESI-MS was utilized for precise identification of the discriminatory phytoconstituents. Fingerprint-efficacy relationship analysis was conducted via an Orthogonal Projection to Latent Structure multivariate model to unravel the bio-efficient markers in terms of α-glucosidase and α-amylase inhibition as main targets. 3,4-Dimethoxycinnamic acid, caffeic acid, isoferulic acid, rosmarinic acid, and quercetin were found to be the main health-relevant markers. A complementary fast, simple and readily available UV spectroscopic method using aluminium chloride as bathochromic shift reagent was used as an independent method for prediction of aforementioned biomarkers using a validated Partial Least Squares Regression model. In addition, palynological analysis was implemented to determine the botanical origin of Egyptian propolis for the first time. Twenty-eight pollen types assigned to 13 families were identified, with Asteraceae being the highest representative one. The investigated samples lacked dominant pollen types, which reflected the multifloral origin of Egyptian propolis. Identification of plant sources of propolis, which directly affect its chemical composition and subsequently its biological efficacy represents an integral part of its standardization.

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution.

Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient's own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.

Measuring axial length of the eye from magnetic resonance brain imaging

BackgroundMetrics derived from the human eye are increasingly used as biomarkers and endpoints in studies of cardiovascular, cerebrovascular and neurological disease. In this context, it is important to account for potential confounding that can arise from differences in ocular dimensions between individuals, for example, differences in globe size.MethodsWe measured axial length, a geometric parameter describing eye size from T2-weighted brain MRI scans using three different image analysis software packages (Mango, ITK and Carestream) and compared results to biometry measurements from a specialized ophthalmic instrument (IOLMaster 500) as the reference standard.ResultsNinety-three healthy research participants of mean age 51.0 ± SD 5.4 years were analyzed. The level of agreement between the MRI-derived measurements and the reference standard was described by mean differences as follows, Mango − 0.8 mm; ITK − 0.5 mm; and Carestream − 0.1 mm (upper/lower 95% limits of agreement across the three tools ranged from 0.9 mm to − 2.6 mm). Inter-rater reproducibility was between − 0.03 mm and 0.45 mm (ICC 0.65 to 0.93). Intra-rater repeatability was between 0.0 mm and − 0.2 mm (ICC 0.90 to 0.95).ConclusionsWe demonstrate that axial measurements of the eye derived from brain MRI are within 3.5% of the reference standard globe length of 24.1 mm. However, the limits of agreement could be considered clinically significant. Axial length of the eye obtained from MRI is not a replacement for the precision of biometry, but in the absence of biometry it could provide sufficient accuracy to act as a proxy. We recommend measuring eye axial length from MRI in studies that do not have biometry but use retinal imaging to study neurodegenerative changes so as to control for differing eye size across individuals.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques.

Background:The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.Objective:To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.Methods:We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.Results:Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.Conclusion:Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images.

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Fluoride-Releasing Self-Etch Adhesives Create Thick ABRZ at the Interface.

A fluoride-releasing adhesive system is expected to promote mineralization of demineralized dentin/enamel around a composite restoration, thereby contributing to the longevity of the restoration. Scanning electron microscopic (SEM) and transmission electron microscopic (TEM) observations revealed the formation of an “acid–base resistant zone” (ABRZ) beneath the hybrid layer when dentin was treated with a self-etch adhesive system. A thicker ABRZ was formed from the upper slope to the end of the outer lesion, indicating greater resistance against an acid-base challenge, when a fluoride-releasing self-etch adhesive system was used. The slope formation of a fluoride-releasing adhesive is believed to be due to fluoride-release from the adhesive. Quantitative assessment of the acid resistance was performed at the interface using the region of interest (ROI) mode of a digital image analysis software package. The area of the ABRZ is influenced by the concentration of fluoride release from the adhesive resin. The threshold of fluoride concentration in the adhesive may exist to influence the morphology of the ABRZ. X-ray absorption fine structure (XAFS) analysis of the dentin treated with different concentrations of NaF-mouth rinses suggested that different fluoride concentrations result in the formation of different chemical compounds, such as fluorapatite and CaF2-like structures, on the dentin surface. This may explain the differences in μTBS values and morphological appearance of the ABRZ. NaF is effective in enhancing the enamel/dentin bond durability and also helps create a high quality of ARBZ to improve the clinical success of restorations.

A new tool for technical standardization of the Ki67 immunohistochemical assay.

Ki67, a nuclear proliferation-related protein, is heavily used in anatomic pathology but has not become a companion diagnostic or a standard-of-care biomarker due to analytic variability in both assay protocols and interpretation. The International Ki67 Working Group in breast cancer has published and has ongoing efforts in the standardization of the interpretation of Ki67, but they have not yet assessed technical issues of assay production representing multiple sources of variation, including antibody clones, antibody formats, staining platforms, and operators. The goal of this work is to address these issues with a new standardization tool. We have developed a cell line microarray system in which mixes of human Karpas 299 or Jurkat cells (Ki67+) with Sf9 (Spodoptera frugiperda) (Ki67-) cells are present in incremental standardized ratios. To validate the tool, six different antibodies, including both ready-to-use and concentrate formats from six vendors, were used to measure Ki67 proliferation indices using IHC protocols for manual (bench-top) and automated platforms. The assays were performed by three different laboratories at Yale and analyzed using two image analysis software packages, including QuPath and Visiopharm. Results showed statistically significant differences in Ki67 reactivity between each antibody clone. However, subsets of Ki67 assays using three clones performed in three different labs show no significant differences. This work shows the need for analytic standardization of the Ki67 assay and provides a new tool to do so. We show here how a cell line standardization system can be used to normalize the staining variability in proliferation indices between different antibody clones in a triple negative breast cancer cohort. We believe that this cell line standardization array has the potential to improve reproducibility among Ki67 assays and laboratories, which is critical for establishing Ki67 as a standard-of-care assay.

Advances in Optical Image Analysis Textural Segmentation in Ironmaking

Optical image analysis is commonly used to characterize different feedstock material for ironmaking, such as iron ore, iron ore sinter, coal and coke. Information is often needed for phases which have the same reflectivity and chemical composition, but different morphology. Such information is usually obtained by manual point counting, which is quite expensive and may not provide consistent results between different petrologists. To perform accurate segmentation of such phases using automated optical image analysis, the software must be able to identify specific textures. CSIRO’s Carbon Steel Futures group has developed an optical image analysis software package called Mineral4/Recognition4, which incorporates a dedicated textural identification module allowing segmentation of such phases. The article discusses the problems associated with segmentation of similar phases in different ironmaking feedstock material using automated optical image analysis and demonstrates successful algorithms for textural identification. The examples cover segmentation of three different coke phases: two types of Inert Maceral Derived Components (IMDC), non-reacted and partially reacted, and Reacted Maceral Derived Components (RMDC); primary and secondary hematite in iron ore sinter; and minerals difficult to distinguish with traditional thresholding in iron ore.

Computed Tomographic Evaluation of the Sacroiliac Joints of Young Working Labrador Retrievers of Various Work Status Groups: Detected Lesions Vary Among the Different Groups and Finite Element Analyses of the Static Pelvis Yields Repeatable Measures of Sacroiliac Ligament Joint Strain.

Musculoskeletal injuries can lead to a working dog being withdrawn from service prior to retirement. During training exercises, young working dogs are often required to perform repetitive tasks, including adoption of an upright posture (or “hupp” task). Non-invasive, quantitative methods would be helpful for supporting research on effects of these repetitive tasks on sacroiliac joints (SIJ). Furthering our understanding of lesions in and biomechanical stresses on the SIJ could provide insight into possible training modifications for minimizing risks of SIJ injury. Aims of this retrospective, secondary analysis, exploratory study were to test hypotheses that (1) mean numbers of SIJ computed tomographic (CT) lesions/dog would differ among work status groups in young working Labrador Retrievers; (2) a methodology for using CT data and finite element analysis (FEA) to quantify SIJ ligament strain in the static canine pelvis would be feasible; and (3) this FEA methodology would yield repeatable measures of SIJ ligament strain. Clinical and CT data for 22 Labrador retriever working dogs, aged 11–48 months, were retrospectively reviewed. Dogs were categorized into three work status groups (Breeder, Detection, Other). A veterinary radiologist who was unaware of dog group status recorded numbers of CT lesions for each SIJ, based on previously published criteria. Mean numbers of SIJ CT lesions/dog were compared among dog work status groups. An a priori FEA model was created from the CT images of one of the dogs using image analysis software packages. Using tissue properties previously published for the human pelvis, various directional loads (n = 8) and forces (48 ligament strain values) were placed on the canine model in five trials. Repeatability was tested using regression analysis. There was a significantly greater mean number of subchondral sclerosis lesions in left SIJ of Breeder vs. Detection dogs, a significantly greater mean number of subchondral cysts in right SIJ for Detection vs. Breeder dogs, and a significantly greater mean number of subchondral cysts in right SIJ of Other vs. Breeder dogs (p < 0.05). Finite element modeling and analysis using CT data was feasible and yielded repeatable results in 47/48 (98%) of tests at each combination of strain, ligament, and side.

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut.

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on the triggering of lever and nose-poke sensors present inside the chambers. While this provides a detailed view of when and how animals perform certain responses, it cannot be used to evaluate behaviors that do not trigger any sensors. As such, assessing how animals position themselves and move inside the chamber is rarely possible. To obtain this information, researchers generally have to record and analyze videos. Manufacturers of operant conditioning chambers can typically supply their customers with high-quality camera setups. However, these can be very costly and do not necessarily fit chambers from other manufacturers or other behavioral test setups. The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. It further describes how to use the image analysis software package DeepLabCut to track the status of a strong light signal, as well as the position of a rat, in videos gathered from an operant conditioning chamber. The former is a great aid when selecting short segments of interest in videos that cover entire test sessions, and the latter enables analysis of parameters that cannot be obtained from the data logs produced by the operant chambers.

SO043MALNUTRITION-INFLAMMATION IS A RISK FACTOR FOR BRAIN ATROPHY RELATIVE COGNITIVE IMPAIRMENT IN MAINTENANCE DIALYSIS PATIENTS

Abstract Background and Aims Cognitive impairment (CI) are prevalent and devastating in dialysis patients, whereas the pathophysiology is not very clear. Brain atrophy may involve in the process of CI. To explore the correlation between brain atrophy and cognitive impairment, as well as the risk factors of brain atrophy, we used the voxel based morphometry (VBM) method to evaluate the changes of brain multi-component volume in maintenance dialysis patients, and analyzed it relationship with detailed cognitive function. Method From July 2013 to July 2014, 181 maintenance dialysis patients in our hospital were enrolled for 3.0T MRI examination and cognitive function evaluation. The statistical parameter map (SPM) 8 software package was used for VBM analysis, and the Monte Carlo simulation method (alphasim method) in the functional neural image analysis software package (AFNI) was used for multiple comparison correction at the cluster level to extract the volume of brain multi-component. Cognitive function was evaluated with MMSE, MoCA, Philadelphia word learning test, Boston Naming Test, semantic fluency test and trial making test. The risk factors for brain volume were explored, and the correlation between brain volume and CI was investigated by regression analysis. Results This study enrolled 181 dialysis patients, including 119 cases of maintenance hemodialysis and 62 cases of peritoneal dialysis. According to MMSE and MoCA, the incidence of cognitive impairment was 22.7% and 66.3% respectively. The mean values gray matter volume and white matter volume were 575.4mm3 and 457.8mm3, respectively. The volume of gray matter, white matter, amygdala, caudate nucleus and hippocampus were positively correlated with the scores of specific cognitive functions such as total, memory, language and execution. Among them, amygdala volume atrophy was significantly related to the decrease of cognitive function such as MMSE (β = 2.81, P = 0.005), MoCA (β = 6.26, P &amp;lt; 0.001). Serum albumin is the risk factor of gray matter volume (β = 5.0, 95% CI = 3.1 to 6.9, P &amp;lt; 0.001) and white matter volume (β = 3.6, 95% CI = 1.7 to 5.5, P &amp;lt; 0.001); Serum Hypersensitive C-reactive protein is the risk factor of gray matter volume (β = -0.9, 95% CI = -1.7 to - 0.1, P = 0.037). Conclusion Brain atrophy in maintenance dialysis patients is closely related to multiple cognitive impairment, and malnutrition - microinflammation may be a risk factor for multi-component brain atrophy.

Clinical Usefulness of SISCOM-SPM Compared to Visual Analysis to Locate the Epileptogenic Zone.

Introduction: Subtraction of ictal-interictal SPECT co-registered to MRI (SISCOM) is a quantification tool that can improve the sensitivity and specificity of the epileptogenic zone (EZ) localization. Commercially available image analysis software packages for SISCOM are costly, and Statistical Parametric Mapping (SPM) could be an alternative free software for the definition of the EZ. There are only a few studies that compare SISCOM using SPM (SISCOM-SPM) with visual analysis.Aim: To compare SISCOM-SPM vs. visual analysis for localization of the EZ in patients with pharmacoresistant focal epilepsies.Materials and methods: We evaluated all our patients with focal epilepsies that underwent ictal and interictal SPECT. We defined the reference standard to locate the EZ by pathology and follow-up (in patients submitted to surgery), or seizure semiology, serial EEG, long-term video-EEG, 18F-FDG PET/CT, and MRI (in patients who were not operated). We compared the location of the EZ by visual analysis of SPECT images and by SISCOM-SPM to the reference standard and classified as concordant, discordant, or partially concordant.Results: We included 23 patients. Visual analysis was concordant with the EZ reference standard in only 13 patients (56.5%), while SISCOM-SPM was concordant in 18 cases (78.3%), providing a 21.8% increase in the location of EZ. However, this difference was not significant due to the small sample size (p = 0.0856).Conclusion: Our preliminary results demonstrate that, in clinical practice, SISCOM-SPM has the potential to add information that might help localize the EZ compared to visual analysis. SISCOM-SPM has a lower cost than other commercially available SISCOM software packages, which is an advantage for developing countries. Studies with more patients are necessary to confirm our findings.

Evaluation of hair follicle parameters using TrichoScale Pro© in healthy dogs: a pilot study.

Trichoscopy, in combination with image analysis software, is a readily available, efficient diagnostic tool for assessing hair follicle parameters of people in vivo. Systems designed for use in human dermatology must be evaluated and validated with animal skin/hair before they can be used for veterinary applications. To evaluate the use of a commercial software package for image analysis of canine hair follicles and to define hair follicle parameters. Thirteen client-owned healthy dogs. Trichoscopic images at x20 magnification were obtained from three shaved body sites (left dorsal head, left thorax, left flank) over a five day period, then analyzed by the software. The image analysis software was able to identify microscopic changes in hair length on a daily basis, calculating a mean hair growth rate of 0.065mm/day. Each of the three body sites had consistent hair growth rates across all days. Anagen and telogen percentages were consistent at each body site by Day 2 (48h post-clipping). Data for mean hair thickness, and primary and secondary hair percentages also are reported. Although it provides less useful data for canine hair coats than for human scalp hair, the system evaluated provides an easy, noninvasive, rapid means for assessing several important canine hair follicle parameters: daily hair growth rates, anagen and telogen percentages, primary and secondary hair percentages, and mean hair thickness. This diagnostic tool could serve to establish breed-specific hair coat standards, evaluating hair follicle changes in various canine alopecic conditions and monitoring coat-promoting treatments in dogs.