Individual Regions Of Interest Research Articles

Perfusion magnetic resonance imaging in Asherman syndrome.

Microvascular scarring compromises the functionality of the endometrium, and vascular flow at the junctional zone (JZ) may be the key to understanding poor reproductive outcomes in women with Asherman syndrome (AS). To investigate whether vascular perfusion of the uterus, measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is impaired in women with intrauterine adhesions (IUA) and AS. A prospective observational cohort pilot study of 23 women with IUA treated with hysteroscopic synecholysis and a control group of two patients with cervix cancer were subject to DCE-MRI with gadolinium to assess uterine vascularity. Twelve regions of interest (ROIs) were allocated on the DCE-MRI image incorporating the JZ, with control ROI placed at the psoas muscle. Individual ROIs were compared to the mean total perfusion (TP) in the same uterus. Pre- and post-operative perfusion analyses were performed on five women. Receiver operator curves (ROC) were used to analyse MRI as a predictor of IUA. There was no significant difference in perfusion; a trend toward reduced perfusion was observed in women with IUA compared to the controls. The ROC was predictive of higher-grade and inoperable IUA. Reduced perfusion on DCE-MRI as assessed by ROC predicted higher-stage AS. The results of this study support further investigation of DCE-MRI as a prognostic tool for AS prior to surgical intervention to assist in providing prognostic guidance for women suffering from AS.

Multiscale co-simulation design pattern for neuroscience applications.

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas

This study aimed to examine the skin temperature (Tsk) variations in five regions of interest (ROI) to assess whether possible disparities between the ROI's Tsk could be associated with specific acute physiological responses during cycling. Seventeen participants performed a pyramidal load protocol on a cycling ergometer. We synchronously measured Tsk in five ROI with three infrared cameras. We assessed internal load, sweat rate, and core temperature. Reported perceived exertion and calves' Tsk showed the highest correlation (r = −0.588; p < 0.01). Mixed regression models revealed that the heart rate and reported perceived exertion were inversely related to calves' Tsk. The exercise duration was directly associated with the nose tip and calf Tsk but inversely related to the forehead and forearm Tsk. The sweat rate was directly related to forehead and forearm Tsk. The association of Tsk with thermoregulatory or exercise load parameters depends on the ROI. The parallel observation of the face and calf Tsk could indicate simultaneously the observation of acute thermoregulatory needs and individual internal load. The separate Tsk analyses of individual ROI appear more suitable to examine specific physiological response than a mean Tsk of several ROI during cycling.

Novel tools for neuronal activity, vessel diameter and red blood cell velocity imaging

Healthy brain function requires a delicate balance between neural activity and local vascular dynamics. Perturbations to this balance, such as vessel diameter changes or fluctuations in red blood cell velocity, are thought to underlie a number of neurodegenerative and neurovascular diseases, including migraine, stroke, and epilepsy. Here, for the first time, we demonstrate high fidelity simultaneous acquisition of vascular and neural circuit dynamics in freely-moving animals using our newly developed nVue dual color miniscope. Our system enables simultaneous imaging of blood flow and neural activity in both superficial and deep-brain structures in awake, behaving rodents. By multiplexing two LEDs at up to 100hz, the nVue enables direct, real-time visualization of both hemodynamics and calcium activity in two channels. Additionally, we have augmented the Inscopix Data Processing Software package to capture time-varying estimates of vessel diameter and red blood cell (RBC) velocity in specific vessels. Vessel diameter is estimated as the full-width half maximum of a Lorentzian function fit to vessel cross-section, while RBC velocity is calculated based on time-lagged correlations between seed-pixels within individual regions of interest and all immediately neighboring pixels. Distances between correlation peaks are then plotted against time. Manual annotation of blood flow data was performed to establish a ground truth against which to validate the algorithm. The nVue blood flow imaging application offers a powerful, turn-key, multisystem workflow for simultaneously interrogating hemodynamics and neural circuit function in freely behaving animals which will profoundly expand the neurovascular coupling toolkit not only for basic and translational research but also for the development of novel preclinical therapeutics. Commercial This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract 5706: ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies

Abstract Tumor-infiltrating lymphocyte (TIL) density has been identified as a prognostic and predictive biomarker in select tumors treated with defined therapies. These observations suggest that TILs may be general markers of patient outcomes, but evidence in support of this hypothesis has been limited by small cohorts. We validated ImmunoPROFILE, a multiplexed immunofluorescence (MIF)-based assay coupled with machine-learning-based image analysis, to identify and quantify tumor cells (cytokeratin, PAX5, PAX8, SOX10), T cells (CD8), T-regulatory cells (FOXP3), exhausted cells (PD-1) and immunosuppressive tumor and immune cells (PD-L1). We applied the MIF panel to specimens from patients collected prospectively over three years and analyzed 2,023 cases across 27 tumor types. The association between biomarkers and overall survival (OS) was investigated using Cox models controlling for patient risk factors such as cancer type, metastatic vs. primary disease, age, and gender. Multivariable biomarker selection was based on likelihood ratios. The assay was highly robust (success rate 97%), reproducible (inter-scanning and intra-staining density controls within 1 SD, inter-staining PD-L1 scores ≤11% CV), and operator-independent (R2 &amp;gt;0.7 to &amp;gt;0.9 for each biomarker and 95% concordance in PD-L1 score-based interpretation between technicians). From whole slide images, a total of 11,932 individual regions of interest were analyzed across the cohort, resulting in &amp;gt;50 million spatially-resolved single cells which were summarized into cell population densities and PD-L1 scores. High densities of CD8+ (&amp;gt;64/mm2, p&amp;lt;0.0001), PD-1+ (&amp;gt;50/mm2, p&amp;lt;0.0001), and FOXP3+ (&amp;gt;30/mm2, p&amp;lt;0.0001) T cells were associated with longer overall survival (OS) irrespective of therapy and across all cancer types. PD-L1 metrics were not associated with OS (p=0.43). Compared to patients with low densities of CD8+ and PD-1+ cells, high densities of at least one of these cell types had better OS (Both high, HR: 0.49, 95% CI: 0.41 - 0.59; CD8+ high, HR: 0.63, (0.48 - 0.82); PD-1+ high, HR: 0.71, (0.54 - 0.93)). The results were consistent in the subset of patients (N=1572) who did not receive immunotherapy (IO). In patients who received IO therapy (N=451), only PD-1+ T-cell density associated with OS (HR: 0.48, (0.36 - 0.65)). To our knowledge, this is the first enterprise-level immune biomarker assay using multiplexed staining, digital imaging, and machine learning to be applied in a prospective manner to clinical specimens at scale. We found that select immune cell densities are prognostic across cancer types and therapies and demonstrated that quantification of multiple cell populations yields better prognostic power than single marker analyses. Citation Format: James Lindsay, Bijaya Sharma, Kristen D. Felt, Anita Giobbie-Hurder, Ian Dryg, Jason L. Weirather, Jennifer Altreuter, Tali Mazor, Priti Kumari, Joao V. Alessi, Ajit J. Nirmal, Michael P. Manos, Ananth R. Kumar, William Lotter, Ethan Cerami, Burce E. Johnson, Neil I. Lindeman, Lynette M. Sholl, Jonathan A. Nowak, Scott J. Rodig. ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5706.

Development of a biomarker signature using grating-coupled fluorescence plasmonic microarray for diagnosis of MIS-C.

Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious condition that can develop 4-6weeks after a school age child becomes infected by SARS-CoV-2. To date, in the United States more than 8,862 cases of MIS-C have been identified and 72 deaths have occurred. This syndrome typically affects children between the ages of 5-13; 57% are Hispanic/Latino/Black/non-Hispanic, 61% of patients are males and 100% have either tested positive for SARS-CoV-2 or had direct contact with someone with COVID-19. Unfortunately, diagnosis of MIS-C is difficult, and delayed diagnosis can lead to cardiogenic shock, intensive care admission, and prolonged hospitalization. There is no validated biomarker for the rapid diagnosis of MIS-C. In this study, we used Grating-coupled Fluorescence Plasmonic (GCFP) microarray technology to develop biomarker signatures in pediatric salvia and serum samples from patients with MIS-C in the United States and Colombia. GCFP measures antibody-antigen interactions at individual regions of interest (ROIs) on a gold-coated diffraction grating sensor chip in a sandwich immunoassay to generate a fluorescent signal based on analyte presence within a sample. Using a microarray printer, we designed a first-generation biosensor chip with the capability of capturing 33 different analytes from 80 of sample (saliva or serum). Here, we show potential biomarker signatures in both saliva and serum samples in six patient cohorts. In saliva samples, we noted occasional analyte outliers on the chip within individual samples and were able to compare those samples to 16S RNA microbiome data. These comparisons indicate differences in relative abundance of oral pathogens within those patients. Microsphere Immunoassay (MIA) of immunoglobulin isotypes was also performed on serum samples and revealed MIS-C patients had several COVID antigen-specific immunoglobulins that were significantly higher than other cohorts, thus identifying potential new targets for the second-generation biosensor chip. MIA also identified additional biomarkers for our second-generation chip, verified biomarker signatures generated on the first-generation chip, and aided in second-generation chip optimization. Interestingly, MIS-C samples from the United States had a more diverse and robust signature than the Colombian samples, which was also illustrated in the MIA cytokine data. These observations identify new MIS-C biomarkers and biomarker signatures for each of the cohorts. Ultimately, these tools may represent a potential diagnostic tool for use in the rapid identification of MIS-C.

Automated analysis of FDG-PET/CT imaging to monitor heterogeneous disease response in metastatic castration-resistant prostate cancer.

251 Background: The heterogeneity of individual sites of disease in prostate cancer is well recognized and increases over time as the therapies administered promote divergent evolution. In clinical practice, this is often depicted in radiology reports as a mixed response, wherein some lesions improve and others progress or emerge, for which individualizing management is challenging. The problem is exacerbated in patients with high volume disease by the inability to concisely and effectively assess overall disease status which may mask the presence of emerging resistance that could benefit from early intervention and/or a change in therapy. Methods: Thirty-one sets of serial baseline and on-treatment FDG-PET/CT images done for the clinical management of patients with progressing mCRPC were analyzed using TRAQinform IQ software (AIQ Solutions). Individual regions of interest (ROI) identified and tracked across imaging time-points were analyzed for a range of features. The univariate prognostic weight of each feature was assessed with Cox regression models. Imaging features from single timepoints, heterogeneity of response features, and PSA values/dynamics were input into the separate TRAQinform Profile software, which was calculated to predict either time on treatment or overall survival using 3-fold cross-validation of a random survival forest. Individual case reviews were performed on select patients including TRAQinform IQ analytics, PSA trends, radiology reports, and physician notes to evaluate the potential additive benefit of the TRAQinform IQ output. Results: In general, imaging features were more strongly correlated with overall survival than PSA dynamics. After iterative feature selection, only imaging features were selected for TRAQinform Profile scores. In the case of predicting OS, the most important features were baseline total lesion glycolysis (TLG), and the number of new/progressing lesions. Patients with high TRAQinform Profile scores had shorter median survival times than those with low scores (630 vs 1326 days, p&lt;0.01, c-index = 0.744). Of particular value was the finding that the software could identify indicators of oligo-progression for which directed radiotherapy could be considered. Also detected were early indications of widespread disease progression represented as changes in total SUV uptake and TRAQinform Profile scores, which could indicate that the patient was no longer benefitting from treatment and a change in therapy was needed. Conclusions: In pilot analysis, the TRAQinform Profile scores generated from baseline and early on-treatment FDG-PET images had treatment-agnostic prognostic value in mCRPC. Further validation is on-going. The TRAQinform IQ software can aid in identifying the presence of resistant lesions in the setting of otherwise grossly stable disease as determined by the treating physician.

Reconstruction of massive bone defects after femoral tumor resection using two new-designed 3D-printed intercalary prostheses: a clinical analytic study with the cooperative utilization of multiple technologies

BackgroundTo reconstruct massive bone defects of the femoral diaphysis and proximal end with limited bilateral cortical bone after joint-preserving musculoskeletal tumor resections, two novel 3D-printed customized intercalary femoral prostheses were applied.MethodsA series of nine patients with malignancies who received these novel 3D-printed prostheses were retrospectively studied between July 2018 and November 2021. The proximal and diaphyseal femur was divided into three regions of interest (ROIs) according to anatomic landmarks, and anatomic measurements were conducted on 50 computed tomography images showing normal femurs. Based on the individual implant-involved ROIs, osteotomy level, and anatomical and biomechanical features, two alternative 3D-printed prostheses were designed. In each patient, Hounsfield Unit (HU) value thresholding and finite element analysis were conducted to identify the bone trabecula and calcar femorale and to determine the stress distribution, respectively. We described the characteristics of each prosthesis and surgical procedure and recorded the intraoperative data. All patients underwent regular postoperative follow-up, in which the clinical, functional and radiographical outcomes were evaluated.ResultsWith the ROI division and radiographic measurements, insufficient bilateral cortical bones for anchoring the traditional stem were verified in the normal proximal femur. Therefore, two 3D-printed intercalary endoprostheses, a Type A prosthesis with a proximal curved stem and a Type B prosthesis with a proximal anchorage-slot and corresponding locking screws, were designed. Based on HU value thresholding and finite element analysis, the 3D-printed proximal stems in all prostheses maximally preserved the trabecular bone and calcar femorale and optimized the biomechanical distribution, as did the proximal screws. With the 3D-printed osteotomy guide plates and reaming guide plates, all patients underwent the operation uneventfully with a satisfactory duration (325.00 ± 62.60 min) and bleeding volume (922.22 ± 222.36 ml). In the follow-up, Harris Hip and Musculoskeletal Tumor Society scores were ameliorated after surgery (P < 0.001 and P < 0.001, respectively), reliable bone ingrowth was observed, and no major complications occurred.ConclusionsTwo novel 3D-printed femoral intercalary prostheses, which achieved acceptable overall postoperative outcomes, were used as appropriate alternatives for oncologic patients with massive bone defects and limited residual bone and increased the opportunities for joint‐preserving tumor resection. Several scientific methodologies utilized in this study may promote the clinical design proposals of 3D-printed implants.

Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features. We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC. This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.

An unsupervised region of interest extraction model for tau PET images and its application in the diagnosis of Alzheimer's disease.

Recently, tau positron-emission tomography (PET) images have been widely used for the diagnosis of Alzheimer's disease (AD). However, existing semi-quantitative uptake value ratios (SUVR) calculation is usually based on group analysis or specific brain regions from existing templates, which cannot detect individual heterogeneity. In this study, we proposed a novel deep learning model; called generative adversarial networks constrained multiple loss autoencoder for tau (GANCMLAE4TAU), to extract individual regions of interest (ROIs) of tau deposition. The basic framework of the proposed model is composed of two encoders, one decoder, and one discriminator. Tau PET images of 327 cognitive normal (CN) subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to train the model and 29 CNs from Huashan Hospital were used as an external validation group. The other 57 AD patients and 83 CNs subjects from ADNI were used in the classification task. The Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) were applied to validate the robustness of our model. In addition, we conducted a receiver operating characteristic curve (ROC) analysis for the SUVR of individual ROIs from the GANCMLAE4TAU model and compared it with SUVR of the whole brain and ROIs from the templates. Our model achieved good SSIM (0.963±0.006), PSNR (35.960±3.458) and MSE (0.0004±0.0003). In ROC analysis, our model had the highest area under curve (AUC) (0.869, 0.809-0.929) in discriminating AD from CN subjects. GANCMLAE4TAU could detect individual ROIs for tau PET images and had the potential to be developed as a novel diagnostic tool in the future. Clinical Relevance- This method can find individual ROIs of tau depositions, so as to achieve more accurate diagnosis of Alzheimer's disease.

Associations between BMI and brain structures involved in food intake regulation in first-episode schizophrenia spectrum disorders and healthy controls

Structural brain differences have been described in first-episode schizophrenia spectrum disorders (FES), and often overlap with those evident in the metabolic syndrome (MetS). We examined the associations between body mass index (BMI) and brain structures involved in food intake regulation in minimally treated FES patients (n = 117) compared to healthy controls (n = 117). The effects of FES diagnosis, BMI and their interactions on our selected prefrontal cortical thickness and subcortical gray matter volume regions of interest (ROIs) were investigated with hierarchical multivariate regressions, followed by post-hoc regressions for the individual ROIs. In a secondary analysis, we examined the relationships of other MetS risk factors and psychopathology with the brain ROIs. Both illness and BMI significantly predicted the grouped prefrontal cortical thickness ROIs, whereas only BMI predicted the grouped subcortical volume ROIs. For the individual ROIs, schizophrenia diagnosis predicted thinner left and right frontal pole and right lateral OFC thickness, and increased BMI predicted thinner left and right caudal ACC thickness. There were no significant main or interaction effects for diagnosis and BMI on any of the individual subcortical volume ROIs. Secondary analyses suggest associations between several brain ROIs and individual MetS risk factors, but not with psychopathology. Our findings indicate differential, independent effects for FES diagnosis and BMI on brain structures. Limited evidence suggests that the BMI effects are more prominent in FES. Exploratory analyses suggest associations between other MetS risk factors and some brain ROIs.

Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers.

Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.

The spatially resolved transcriptional profile of acute T cell–mediated rejection in a kidney allograft

Analysis of the transcriptional profile of graft biopsies represents a promising strategy to study T cell-mediated-rejection (TCMR), also known as acute cellular rejection. However, bulk RNA sequencing of graft biopsies may not capture the focal nature of acute rejection. Herein, we used the whole exome GeoMX Digital Space Profiling platform to study five tubular and three glomerular regions of interest in the kidney graft biopsy from a patient with a chronic-active TCMR episode and in analogous areas from two different normal kidney control biopsies. All kidney sections were from paraffin blocks. Overall, inflammatory genes were significantly upregulated in the tubular areas of the TCMR biopsy and showed an enrichment for gene-ontology terms associated with T-cell activation, differentiation, and proliferation. Enrichment analysis of the 100 genes with the highest coefficient of variation across the TCMR tubular regions of interest revealed that these highly variable genes are involved in kidney development and injury and interestingly do not associate with the 2019 Banff classification pathology scores within the individual regions of interest. Spatial transcriptomics allowed us to unravel a previously unappreciated variability across different areas of the TCMR biopsy related to the graft response to the alloimmune attack, rather than to the immune cells. Thus, our approach has the potential to decipher clinically relevant, new pathogenic mechanisms, and therapeutic targets in acute cellular rejection and other kidney diseases with a focal nature.

In-vitro trabecular bone damage following mono- and bicortical mini implants anchorage in mini-implant assisted rapid palatal expansion (MARPE)

To assess in-vitro trabecular bone damage following mono- and bicortical mini-implant (MI) anchorage in mini-implant assisted rapid palatal expansion (MARPE). Sixteen self-drilling MI (four MARPE appliances) were distributed in two groups according to bone insertion (monocortical and bicortical) in bovine rib. The device was activated five times (0.5mm each). Trabecular bone damage was assessed using micro-CT scans made at baseline and after each activation by trabecular spacing parameter (Tb.Sp) (distance [mm] between the trabecular bone structure). These measurements were made in five different regions of interest (ROI) surrounding the screw (whole, superior, inferior, anterior and posterior). Two-way ANOVA with Tukey post-hoc analysis (α=0.05) was used to evaluate the effect of insertion type (monocortical vs. bicortical) and activation cycle (0-5) on trabecular damage. The time effect was evaluated using ANOVA-MR test effect with Bonferroni correction (α=0.003). The micro-CT images were also examined qualitatively. When analysing the individual ROIs, only the superior ROI had a significant difference (P<0.003) beginning at the fourth activation cycle. For the monocortical group, trabecular spacing was affected when the whole ROI was analysed beginning at the fourth activation cycle, while for the superior ROI, this difference became apparent beginning with the third activation cycle (P<0.003). For the qualitative analysis, it seems that only monocortical anchorage influences the trabecular bone in the superior area. Monocortical anchorage is more susceptible to bone damage around the MIs, with the superior (cervical) region most strongly affected.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification.

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images.

Simple SummaryIn this study, the thermal patterns of horses and donkeys in infrared thermography (IRT) images are analyzed and compared. Thermal patterns are defined as statistically significant differences between groups of regions of interest (ROIs) corresponding to underlying large muscles. The dataset used in the experiments consists of images of healthy and rested animals: sixteen horses and eighteen donkeys. Thermal patterns between species are compared, and the results are discussed along with special cases of animals identified as outliers. The results support the thesis about the similarities in the thermal patterns of horses and donkeys.Infrared thermography (IRT) is a valuable diagnostic tool in equine veterinary medicine; however, little is known about its application to donkeys. This study aims to find patterns in thermal images of donkeys and horses and determine if these patterns share similarities. The study is carried out on 18 donkeys and 16 horses. All equids undergo thermal imaging with an infrared camera and measurement of the skin thickness and hair coat length. On the class maps of each thermal image, fifteen regions of interest (ROIs) are annotated and then combined into 10 groups of ROIs (GORs). The existence of statistically significant differences between surface temperatures in GORs is tested both “globally” for all animals of a given species and “locally” for each animal. Two special cases of animals that differed from the rest are also discussed. The results indicate that the majority of thermal patterns are similar for both species; however, average surface temperatures in horses ( °C) are higher than in donkeys ( °C). This could be related to differences in the skin thickness and hair coat. The patterns of both species are associated with GORs, rather than with an individual ROI, and there is a higher uniformity in the donkeys’ patterns.

Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases.

Magnetic resonance imaging (MRI) constitutes a powerful outcome measure in neuromuscular disorders, yet there is a broad diversity of approaches in data acquisition and analysis. Since each neuromuscular disease presents a specific pattern of muscle involvement, the recommended analysis is assumed to be the muscle-by-muscle approach. We, therefore, performed a comparative analysis of different segmentation approaches, including global muscle segmentation, to determine the best strategy for evaluating disease progression. In 102 patients (21 immune-mediated necrotizing myopathy/IMNM, 21 inclusion body myositis/IBM, 10 GNE myopathy/GNEM, 19 Duchenne muscular dystrophy/DMD, 12 dysferlinopathy/DYSF, 7 limb-girdle muscular dystrophy/LGMD2I, 7 Pompe disease, 5 spinal muscular atrophy/SMA), two MRI scans were obtained at a 1-year interval in thighs and lower legs. Regions of interest (ROIs) were drawn in individual muscles, muscle groups, and the global muscle segment. Standardized response means (SRMs) were determined to assess sensitivity to change in fat fraction (ΔFat%) in individual muscles, muscle groups, weighted combinations of muscles and muscle groups, and in the global muscle segment. Global muscle segmentation gave high SRMs for ΔFat% in thigh and lower leg for IMNM, DYSF, LGMD2I, DMD, SMA, and Pompe disease, and only in lower leg for GNEM and thigh for IBM. Global muscle segment Fat% showed to be sensitive to change in most investigated neuromuscular disorders. As compared to individual muscle drawing, it is a faster and an easier approach to assess disease progression. The use of individual muscle ROIs, however, is still of interest for exploring selective muscle involvement. • MRI-based evaluation of fatty replacement in muscles is used as an outcome measure in the assessment of 1-year disease progression in 8 different neuromuscular diseases. • Different segmentation approaches, including global muscle segmentation, were evaluated for determining 1-year fat fraction changes in lower limb skeletal muscles. • Global muscle segment fat fraction has shown to be sensitive to change in lower leg and thigh in most of the investigated neuromuscular diseases.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To facilitate and spread the usage of analysis tools, we provide examples of such pipelines, including a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Comparison of surface thermal patterns of horses and donkeys in IRT images

Infrared thermography (IRT) is a valuable diagnostic tool in equine veterinary medicine however, little is known about its application in donkeys. The aim was to find patterns in thermal images of donkeys and horses, and determine if these patterns share similarities. The study was carried out on 18 donkeys and 16 horses. All equids underwent thermal imaging with an infrared camera and measuring the skin thickness and hair coat length. On the class maps of each thermal image, 15 regions of interest (ROIs) were annotated and then combined into 10 groups of ROIs (GORs). The existence of statistically significant differences between surface temperatures in GORs was tested both `globally' for all animals of a given species and `locally' for each animal. Two special cases of animals that differ from the rest were also discussed. Our results indicated that the majority of thermal patterns are similar for both species however, average surface temperatures in horses are higher than in donkeys. It may be related to differences in the skin and hair coat. We concluded, the patterns of both species are associated with GORs, rather than an individual ROI, with higher uniformity of donkeys patterns.