A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

Feasibility of the Threshold-Based Quantification of Myocardial Fibrosis on Cardiac CT as a Prognostic Marker in Nonischemic Dilated Cardiomyopathy.

OBJECTIVE: This study investigated the feasibility and prognostic relevance of threshold-based quantification of myocardial delayed enhancement (MDE) on CT in patients with nonischemic dilated cardiomyopathy (NIDCM). MATERIALS AND METHODS: Forty-three patients with NIDCM (59.3 ± 17.1 years; 21 male) were included in the study and underwent cardiac CT and MRI. MDE was quantified manually and with a threshold-based quantification method using cutoffs of 2, 3, and 4 standard deviations (SDs) on three sets of CT images (100 kVp, 120 kVp, and 70 keV). Interobserver agreement in MDE quantification was assessed using the intraclass correlation coefficient (ICC). Agreement between CT and MRI was evaluated using the Bland-Altman method and the concordance correlation coefficient (CCC). Patients were followed up for the subsequent occurrence of the primary composite outcome, including cardiac death, heart transplantation, heart failure hospitalization, or appropriate use of an implantable cardioverter-defibrillator. The Kaplan-Meier method was used to estimate event-free survival according to MDE levels. RESULTS: Late gadolinium enhancement (LGE) was observed in 29 patients (67%, 29/43), and the mean LGE found with the 5-SD threshold was 4.1% ± 3.6%. The 4-SD threshold on 70-keV CT showed excellent interobserver agreement (ICC = 0.810) and the highest concordance with MRI (CCC = 0.803). This method also yielded the smallest bias with the narrowest range of 95% limits of agreement compared to MRI (bias, -0.119%; 95% limits of agreement, -4.216% to 3.978%). During a median follow-up of 1625 days (interquartile range, 712-1430 days), 10 patients (23%, 10/43) experienced the primary composite outcome. Event-free survival significantly differed between risk subgroups divided by the optimal MDE cutoff of 4.3% (log-rank P = 0.005). CONCLUSION: The 4-SD threshold on 70-keV monochromatic CT yielded results comparable to those of MRI for quantifying MDE as a marker of myocardial fibrosis, which showed prognostic value in patients with NIDCM.

Innovations in Diabetic Macular Edema Management: A Comprehensive Review of Automated Quantification and Anti-vascular Endothelial Growth Factor Intervention.

Diabetic macular edema (DME) poses a significant threat to the vision and quality of life of individuals with diabetes. This comprehensive review explores recent advancements in DME management, focusing on integrating automated quantification techniques and anti-vascular endothelial growth factor (anti-VEGF) interventions. The review begins with an overview of DME, emphasizing its prevalence, impact on diabetic patients, and current challenges in management. It then delves into the potential of automated quantification, leveraging machine learning and artificial intelligence to improve early detection and monitoring. Concurrently, the role of anti-VEGF therapies in addressing the underlying vascular abnormalities in DME is scrutinized. The review synthesizes vital findings, highlighting the implications for the future of DME management. Promising outcomes from recent clinical trials and case studies are discussed, providing insights into the evolving landscape of personalized medicine approaches. The conclusion underscores the transformative potential of these innovations, calling for continued research, collaboration, and integration of these advancements into clinical practice. This review aims to serve as a roadmap for researchers, clinicians, and industry stakeholders, fostering a collective effort to enhance the precision and efficacy of DME management.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates.

INTRODUCTION: The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES: To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS: An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS: The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS: The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Semi-automated quantification of vitreal hyperreflective foci in SD-OCT and their relevance in patients with peripheral retinal breaks.

BACKGROUND: Retinal breaks (RB) are emergencies that require treatment to prevent progression of rhegmatogenous retinal detachment. Vitreal hyperreflective foci (VHF) representing migration of RPE cell clusters or interphotoreceptor matrix from the RB are potential biomarkers. The aim of this study is to investigate VHF in RB-patients using SD-OCT. METHODS: The retrospective cross-sectional study included RB patients from our Department of Ophthalmology, HSK Wiesbaden who underwent macular SD-OCT (SPECTRALIS®, Heidelberg Engineering, Germany) on both eyes. VHF, defined and quantified as foci that differ markedly in size and reflectivity from the background speckle pattern, were assessed for presence and frequency. The RB-affected eyes were the study group (G1), the partner eyes the control group (G2). RESULTS: 160 consecutive patients with RB were included. Age was 60 ± 10.2 years (52% female). 89.4% of G1 and 87.5% of G2 were phakic (p = 0.73). 94.4% (n = 151) were symptomatic. Symptom duration was 8.0 ± 10.1 days in G1, 94.4% (n = 151) showed VHF versus 5.6% (p < 0.0001) in G2, of which 75% (n = 6) showed asymptomatic lattice degenerations. Detectable VHF showed a strong association of OR = 320 (95% CI, 110-788, p < 0.0001)) with respect to symptomatic RB. Sensitivity and specificity were 94.7% and 94.7%, respectively. CONCLUSIONS: Most eyes with symptomatic RB show vitreal VHF in SD-OCT. Detected VHF are strongly associated with RB, and our semi-automated greyscale reflectivity analysis indicates that VHF likely originate from photoreceptor complexes torn out of the RB area that migrate into the vitreous cavity. The presence of VHF may indicate RB and should lead to a thorough fundus examination in both symptomatic and asymptomatic cases.

An Automated Quantification Tool for Angiogenic Sprouting From Endothelial Spheroids.

The process of sprouting angiogenesis can be measured in vitro using endothelial cells in sprouting assays such as the fibrin bead assay and the spheroid-based assay. While the technical aspects of these sprouting assays have been well-optimized, the analysis aspects have been limited to manual methods, which can be time-consuming and difficult to reproduce. Here, we developed an automated analysis tool called AQuTAS to quantify sprouting parameters from the spheroid-based sprouting assay. We trained and validated the algorithm on two subsets of data, and tested its sensitivity by measuring changes in sprouting parameters over a range of concentrations of pro- and antiangiogenic compounds. Our results demonstrate that the algorithm detects known differences in sprouting parameters in endothelial spheroids treated with pro- and antiangiogenic compounds. Moreover, it is sensitive to biological changes that are ≥40%. Among the five quantified parameters, cumulative sprout length is likely the most discriminative parameter for measuring differences in sprouting behavior because it had the highest effect size (>1.5 Cohen's d). In summary, we have generated an automated tool that quantifies sprouting parameters from the spheroid-based assay in a reproducible and sensitive manner.

Semi-Automated Quantification of Retinal and Choroidal Biomarkers in Retinal Vascular Diseases: Agreement of Spectral-Domain Optical Coherence Tomography with and without Enhanced Depth Imaging Mode.

BACKGROUND: We compared with and without enhanced depth imaging mode (EDI) in semi-automated quantification of retinal and choroidal biomarkers in optical coherence tomography (OCT) in patients with diabetic retinopathy (DR) or retinal vein occlusion (RVO) complicated by macular edema. We chose to study three OCT biomarkers: the numbers of hyperreflective foci (HF), the ellipsoid zone reflectivity ratio (EZR) and the choroidal vascularity index (CVI), all known to be correlated with visual acuity changes or treatment outcomes. METHODS: In a single examination, one eye of each patient (n = 60; diabetic retinopathy: n = 27, retinal vein occlusion: n = 33) underwent macular 870 nm spectral domain-OCT (SD-OCT) B-scans without and with EDI mode. Semi-automated quantification of HF, EZR and CVI was applied according to preexisting published protocols. Paired Student's t-test or Wilcoxon rank-sum test was used to test for differences in subgroups. Intraclass correlation coefficient (ICC) and Bland-Altman plots were applied to describe the agreement between quantification in EDI and conventional OCT mode. The effect of macular edema on semi-automated quantification was evaluated. RESULTS: For the entire cohort, quantification of all three biomarkers was not significantly different in SD-OCT scans with and without EDI mode (p > 0.05). ICC was 0.78, 0.90 and 0.80 for HF, EZR and CVI. The presence of macular edema led to significant differences in the quantification of hyperreflective foci (without EDI: 80.00 ± 33.70, with EDI: 92.08 ± 38.11; mean difference: 12.09, p = 0.03), but not in the quantification of EZR and CVI (p > 0.05). CONCLUSION: Quantification of EZR and CVI was comparable whether or not EDI mode was used. In conclusion, both retinal and choroidal biomarkers can be quantified from one single 870 nm SD-OCT EDI image.

Quantifying the Quality of Web-Based Health Information on Student Health Center Websites Using a Software Tool: Design and Development Study.

BACKGROUND: The internet has become a major source of health information, especially for adolescents and young adults. Unfortunately, inaccurate, incomplete, or outdated health information is widespread on the web. Often adolescents and young adults turn to authoritative websites such as the student health center (SHC) website of the university they attend to obtain reliable health information. Although most on-campus SHC clinics comply with the American College Health Association standards, their websites are not subject to any standards or code of conduct. In the absence of quality standards or guidelines, monitoring and compliance processes do not exist for SHC websites. Thus, there is no oversight of the health information published on SHC websites by any central governing body. OBJECTIVE: The aim of this study is to develop, describe, and validate an open-source software that can effectively and efficiently assess the quality of health information on SHC websites in the United States. METHODS: Our cross-functional team designed and developed an open-source software, QMOHI (Quantitative Measures of Online Health Information), that assesses information quality for a specified health topic from all SHC websites belonging to a predetermined list of universities. The tool was designed to compute 8 different quality metrics that quantify various aspects of information quality based on the retrieved text. We conducted and reported results from 3 experiments that assessed the QMOHI tool in terms of its scalability, generalizability in health topics, and robustness to changes in universities' website structure. RESULTS: Empirical evaluation has shown the QMOHI tool to be highly scalable and substantially more efficient than manually assessing web-based information quality. The tool's runtime was dominated by network-related tasks (98%), whereas the metric computations take <2 seconds. QMOHI demonstrated topical versatility, evaluating SHC website information quality for four disparate and broad health topics (COVID, cancer, long-acting reversible contraceptives, and condoms) and two narrowly focused topics (hormonal intrauterine device and copper intrauterine device). The tool exhibited robustness, correctly measuring information quality despite changes in SHC website structure. QMOHI can support longitudinal studies by being robust to such website changes. CONCLUSIONS: QMOHI allows public health researchers and practitioners to conduct large-scale studies of SHC websites that were previously too time- and cost-intensive. The capability to generalize broadly or focus narrowly allows a wide range of applications of QMOHI, allowing researchers to study both mainstream and underexplored health topics. QMOHI's ability to robustly analyze SHC websites periodically promotes longitudinal investigations and allows QMOHI to be used as a monitoring tool. QMOHI serves as a launching pad for our future work that aims to develop a broadly applicable public health tool for web-based health information studies with potential applications far beyond SHC websites.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®.

OBJECTIVES: QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS: Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS: The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS: QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS: • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry.

BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.

Quantification of increased MUC5AC expression in airway mucus of smoker using an automated image-based approach.

Microscopic analysis of mucus quantity and composition is crucial in research and diagnostics on muco-obstructive diseases. Currently used image-based methods are unable to extract concrete numeric values of mucosal proteins, especially on the expression of the key mucosal proteins MUC5AC and MUC5B. Since their levels increase under pathologic conditions such as extensive exposure to cigarette smoke, it is imperative to quantify them to improve treatment strategies of pulmonary diseases. This study presents a simple, image-based, and high-processing computational method that allows determining the ratio of MUC5AC and MUC5B within the overall airway mucus while providing information on their spatial distribution. The presented pipeline was optimized for automated downstream analysis using a combination of bright field and immunofluorescence imaging suitable for tracheal and bronchial tissue samples, and air-liquid interface (ALI) cell cultures. To validate our approach, we compared tracheal tissue and ALI cell cultures of isolated primary normal human bronchial epithelial cells derived from smokers and nonsmokers. Our data indicated 18-fold higher levels of MUC5AC in submucosal glands of smokers covering about 8% of mucosal areas compared to <1% in nonsmoking individuals, confirming results of previous studies. We further identified a subpopulation of nonsmokers with slightly elevated glandular MUC5AC levels suggesting moderate exposure to second-hand smoke or fine particulate air pollution. Overall, this study demonstrates a novel, user-friendly and freely available tool for digital pathology and the analysis of therapeutic interventions tested in ALI cell cultures.

Automated Quantification of Subcellular Particles in Myogenic Progenitors.

Fluorescence microscopy is a powerful tool enabling the visualization of protein localization within cells. In this article, we outline an automated and non-biased way to detect and quantify subcellular particles using immunocytochemistry, fluorescence microscopy, and the program CellProfiler. We discuss the examination of two types of subcellular particles: messenger ribonucleoprotein (mRNP) granules, namely processing bodies and stress granules, and autophagosomes. Fluorescent microscopy Z-stacks are acquired and deconvolved, and maximum intensity images are generated. The number of subcellular particles per cell is then quantified using the described CellProfiler pipeline. We also explain how to isolate primary myoblast progenitor cells from mice, which were used to obtain the presented results. Last, we discuss the critical parameters to be considered for each of these techniques. Both mRNP granules and autophagosomes play important roles in sequestering intracellular cargo, such as messenger RNAs and RNA-binding proteins for mRNP granules and cytoplasmic waste for autophagosomes. The methods outlined in this article are widely applicable for studies relating to subcellular particle formation, localization, and flux during homeostasis, following stimuli, and during disease. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Immunofluorescence microscopy of messenger ribonucleoprotein granules in primary myoblasts Alternate Protocol: Immunofluorescence microscopy of autophagosomes in primary myoblasts Support Protocol: Isolation of primary myoblasts from mice Basic Protocol 2: Automated quantification of subcellular particles.

Live Imaging and Quantification of Neutrophil Extracellular Trap Formation.

NeutrophilExtracellular Trap (NET) formation (NETosis) is a unique process that occurs in response to numerous stimuli. To investigate NETosis, we created a method that can be used easily without the need for complex programming abilities and commercial software packages. This article describes a fully automated assay to quantify NETosis using fluorescence live imaging on an automated widefield inverted microscope. Herein, we describe (1) sample preparation, (2) required equipment for automated acquisition, and finally (3) analysis of NETosis using the readily available image analysis software Fiji (ImageJ2). This protocol can be adapted to evaluate NETosis after different stimuli, and can be easily modified to allow high-throughput acquisition and analysis using a multi-well plate format. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Neutrophil isolation and plate setup Basic Protocol 2: Microscope and acquisition setup for automated high throughput imaging Basic Protocol 3: Analysis of NETosis and apoptosis data.

Usefulness of the measurement of neurite outgrowth of primary sensory neurons to study cancer-related painful complications.

Abnormal outgrowth of sensory nerves is one of the important contributors to pain associated with cancer and its treatments. Primary neuronal cultures derived from dorsal root ganglia (DRG) have been widely used to study pain-associated signal transduction and electrical activity of sensory nerves. However, there are only a few studies using primary DRG neuronal culture to investigate neurite outgrowth alterations due to underlying cancer-related factors and chemotherapeutic agents. In this study, primary DRG sensory neurons derived from mouse, non-human primate, and human were established in serum and growth factor-free conditions. A bovine serum albumin gradient centrifugation method improved the separation of sensory neurons from satellite cells. The purified DRG neurons were able to maintain their heterogeneous subpopulations, and displayed an increase in neurite growth when exposed to cancer-derived conditioned medium, while they showed a reduction in neurite length when treated with a neurotoxic chemotherapeutic agent. Additionally, a semi-automated quantification method was developed to measure neurite length in an accurate and time-efficient manner. Finally, these exogenous factors altered the gene expression patterns of murine primary sensory neurons, which are related to nerve growth, and neuro-inflammatory pain and nociceptor development. Together, the primary DRG neuronal culture in combination with a semi-automated quantification method can be a useful tool for further understanding the impact of exogenous factors on the growth of sensory nerve fibers and gene expression changes in sensory neurons.

The Prognostic Value of a Validated and Automated Intravascular Ultrasound-Derived Calcium Score.

BACKGROUND: Coronary calcification has been linked to cardiovascular events. We developed and validated an algorithm to automatically quantify coronary calcifications on intravascular ultrasound (IVUS). We aimed to assess the prognostic value of an IVUS-calcium score (ICS) on patient-oriented composite endpoint (POCE). METHODS: We included patients that underwent coronary angiography plus pre-procedural IVUS imaging. The ICS was calculated per patient. The primary endpoint was a composite of all-cause mortality, stroke, myocardial infarction, and revascularization (POCE). RESULTS: In a cohort of 408 patients, median ICS was 85. Both an ICS ≥ 85 and a 100 unit increase in ICS increased the risk of POCE at 6-year follow-up (adjusted hazard ratio (aHR) 1.51, 95%CI 1.05-2.17, p value = 0.026, and aHR 1.21, 95%CI 1.04-1.41, p value = 0.014, respectively). CONCLUSIONS: The ICS, calculated by a validated automated algorithm derived from routine IVUS pullbacks, was strongly associated with the long-term risk of POCE.

Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry.

Deciphering the spatial composition of cells in tissues is essential for detailed understanding of biological processes in health and disease. Recent technological advances enabled the assessment of the enormous complexity of tissue-derived parameters by highly multiplexed tissue imaging (HMTI), but elaborate machinery and data analyses are required. This severely limits broad applicability of HMTI. Here we demonstrate for the first time the application of ChipCytometry technology, which has unique features for widespread use, on formalin-fixed paraffin-embedded samples, the most commonly used storage technique of clinically relevant patient specimens worldwide. The excellent staining quality permits workflows for automated quantification of signal intensities, which we further optimized to compensate signal spillover from neighboring cells. In combination with the high number of validated markers, the reported platform can be used from unbiased analyses of tissue composition to detection of phenotypically complex rare cells, and can be easily implemented in both routine research and clinical pathology.

Computer-aided quantification of non-contrast 3D black blood MRI as an efficient alternative to reference standard manual CT angiography measurements of abdominal aortic aneurysms.

BACKGROUND: Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. PURPOSE: To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA). MATERIALS AND METHODS: Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson correlation and Bland-Altman analyses. Finally, automated measurements from non-contrast 3D black blood MRI were evaluated against manual CTA measurements using the Wilcoxon test, Pearson correlation and Bland-Altman analyses. RESULTS: Semi-automatic segmentations had excellent agreement with manual segmentations (lumen DSC: 0.91 ± 0.03 and 0.94 ± 0.03; total aneurysm DSC: 0.92 ± 0.02 and 0.94 ± 0.03, for black blood MRI and CTA, respectively). Automated volume and maximum diameter measurements also had excellent correlation to their manual counterparts for both black blood MRI (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001) and CTA (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001). Compared to manual CTA measurements, bias and limits of agreement (LOA) for automated MRI measurements (lumen volume: 1.49, [-4.19 7.17] cm3; outer wall volume: -2.46, [-14.05 9.13] cm3; maximal diameter: 0.08, [-6.51 6.67] mm) were largely equivalent to those of manual MRI measurements, particularly for maximum AAA diameter (lumen volume: 0.73, [-6.47 7.93] cm3; outer wall volume: 0.98, [-10.54 12.5] cm3; maximal diameter: 0.08, [-3.67 3.83] mm). CONCLUSION: Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.

Automated quantification of spike-wave activity may be used to predict the development of electrical status epilepticus in sleep (ESES) in children with perinatal stroke.

OBJECTIVE: Continuous spike and wave in slow-wave sleep (CSWS), an epileptic encephalopathy, occurs after perinatal stroke where it is associated with cognitive decline. CSWS features a distinct EEG pattern, electrical status epilepticus in sleep (ESES). Biomarkers for the prediction of ESES have not been identified but will facilitate earlier diagnosis and treatment. We hypothesized that spike-frequency and differences in power spectra would be predictive of subsequent ESES. METHODS: A cross-sectional study comparing EEG spike-frequency and Power before the development of ESES in patients with perinatal stroke, patients with focal epilepsy, and appropriate controls. RESULTS: 43 patients met the inclusion criteria; 11 stroke-ESES, 10 stroke controls, 14 epilepsy-ESES, 8 epilepsy controls. ESES patients had higher pre-diagnosis mean spike-frequency (24.0 ± 24 versus 6.6 ± 9.1 SW/min, p = 0.002) than patients that did not develop ESES; these differences present ~ 3 years before ESES diagnosis. Pre-diagnosis, normalized delta power (1-4 Hz) was higher in the stroke-ESES group (105.7 ± 58 dB/Hz) compared to stroke controls (57.4 ± 45 dB/Hz, p = 0.036). CONCLUSION: Spike-frequency and delta power may represent EEG biomarkers of the risk of developing ESES in children with perinatal stroke. SIGNIFICANCE: EEG biomarkers may be used by clinicians to assess which patients are more at-risk for ESES. Using spike-frequency, clinicians may be able to identify patients at risk of developing ESES.

Automatic Quantification of Cardiomyocyte Dimensions and Connexin 43 Lateralization in Fluorescence Images.

Cardiomyocytes' geometry and connexin 43 (CX43) amount and distribution are structural features that play a pivotal role in electrical conduction. Their quantitative assessment is of high interest in the study of arrhythmias, but it is usually hampered by the lack of automatic tools. In this work, we propose a software algorithm (Myocyte Automatic Retrieval and Tissue Analyzer, MARTA) to automatically detect myocytes from fluorescent microscopy images of cardiac tissue, measure their morphological features and evaluate the expression of CX43 and its degree of lateralization. The proposed software is based on the generation of cell masks, contouring of individual cells, enclosing of cells in minimum area rectangles and splitting of these rectangles into end-to-end and middle compartments to estimate CX43 lateral-to-total ratio. Application to human ventricular tissue images shows that mean differences between automatic and manual methods in terms of cardiomyocyte length and width are below 4 µm. The percentage of lateral CX43 also agrees between automatic and manual evaluation, with the interquartile range approximately covering from 3% to 30% in both cases. MARTA is not limited by fiber orientation and has an optimized speed by using contour filtering, which makes it run hundreds of times faster than a trained expert. Developed for CX43 studies in the left ventricle, MARTA is a flexible tool applicable to morphometric and lateralization studies of other markers in any heart chamber or even skeletal muscle. This open-access software is available online.

Automated Quantitative Assessment of Coronary Calcification Using Intravascular Ultrasound.

Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.