Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.

Increased proliferation is associated with CNS invasion in meningiomas

Introduction Meningiomas are the most common benign intracranial neoplasms. CNS invasion in meningiomas has been integrated into the 2016 WHO classification of CNS tumors as a stand-alone criterion for atypia. Since then, its prognostic impact has been debated based on contradictory results from retrospective analyses. The aim of the study was to elucidate whether histopathological evidence of CNS invasion is associated with increased proliferative potential. Methods We have conducted a quantified measurement of the proliferation marker Ki67 and analyzed its association with CNS invasion determined by histology together with other established prognostic markers of progression. Routine, immunohistochemical staining for Ki67 were digitalized and automatic quantification was done using Image J software. Results Overall, 1718 meningiomas were assessed. Histopathological CNS invasion was seen in 108 cases (6.7%). Uni- and multivariate analysis revealed a significantly higher Ki67 proliferation rate in meningiomas with CNS invasion (p < 0.0001 and p = 0.0098, respectively). Conclusions Meningiomas with histopathological CNS invasion show a higher proliferative activity.

Macros to Quantify Exosome Release and Autophagy at the Neuromuscular Junction of Drosophila Melanogaster

Automatic quantification of image parameters is a powerful and necessary tool to explore and analyze crucial cell biological processes. This article describes two ImageJ/Fiji automated macros to approach the analysis of synaptic autophagy and exosome release from 2D confocal images. Emerging studies point out that exosome biogenesis and autophagy share molecular and organelle components. Indeed, the crosstalk between these two processes may be relevant for brain physiology, neuronal development, and the onset/progression of neurodegenerative disorders. In this context, we describe here the macros “Autophagoquant” and “Exoquant” to assess the quantification of autophagosomes and exosomes at the neuronal presynapse of the Neuromuscular Junction (NMJ) in Drosophila melanogaster using confocal microscopy images. The Drosophila NMJ is a valuable model for the study of synapse biology, autophagy, and exosome release. By use of Autophagoquant and Exoquant, researchers can have an unbiased, standardized, and rapid tool to analyze autophagy and exosomal release in Drosophila NMJ.Code available at: https://github.com/IreneSaMi/Exoquant-Autophagoquant

Machine learning analysis of chest CT to detect cardiac abnormalities in COVID19

Introduction Standard, un-gated chest CT can be used as the basis of detailed segmentation of the atrial and ventricular cardiac chambers. In conditions such as COVID19 where dedicated cardiac imaging may be hazardous or unavailable atlas-based machine learning tools allow automatic quantification of cardiac morphology and may allow early detection of abnormalities. Purpose To develop an automated screening tool to detect cardiac changes associated with COVID19 on chest/lung CT to allow early treatment and appropriate selection of patients for dedicated cardiac imaging. Methods A previously validated atlas-based cardiac contouring algorithm was modified to work within the setting of variable and severe lung pathology. The modified technique was used to segment the left and right atria and ventricles from non-contrast CT scans. We applied the developed algorithm to the Moscow University COVID19 CT dataset. 1110 scans were available. COVID19 severity was graded 0 to 4. Grade 4 was not used in analysis due to insufficient numbers. Cardiac chamber sizes were compared according to COVID19 severity status. In a limited cohort of repeat studies, the feasibility of polar mapping to demonstrated serial morphological change was tested. Results A statistically significant increase of average cardiac chamber volumes was noted relative to mild Grade 0 COVID19 at every incremental severity grade (Figure 1). Changes in average ventricular volumes were greater (up to 15.2% and 16.9% for left and right ventricles) than changes in atrial volumes (up 12.1% and 7.6% for left and right atria). Automated quantification was successful in the large majority of cases and inter-patient polar mapping of sequential data to detect progressive chamber enlargement appears feasible (Figure 2). Conclusion Machine learning methods permit automatic quantification of cardiac chamber size from standard lung CT scans. Cardiac changes on lung CT examinations may be used to identify cardiac abnormalities at an early stage and could be useful to triage individuals for dedicated cardiac investigations. With further refinement, this method may be useful to detect and track temporal cardiac changes in COVID19, as well as in other pulmonary pathology and conditions in which chest CT is routinely used. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): SPHERE Research consortium Figure 1 Figure 2

Deep Learning Segmentation of Glomeruli on Kidney Donor Frozen Sections

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. This work aims to develop deep learning (DL) models to quantify non-sclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution (n=123) and at external institutions (n=135) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (Ratio: 0.8:0.2) and external WSIs were used as an independent testing dataset. Non-sclerotic (n=22767) and sclerotic (n=1366) glomeruli were manually annotated by study pathologists on all WSIs. A 9-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of non-sclerotic and sclerotic glomeruli. DL-derived, manual segmentation and reported glomerular count (standard of care) were compared. Results: The average Dice Similarity Coefficient testing was 0.90 and 0.83. and the F1, Recall, and Precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for non-sclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. This work represents the first step toward new protocols for the evaluation of donor kidney biopsies.

Automatic Quantification of Atherosclerosis in Contrast-Enhanced MicroCT Scans of Mouse Aortas Ex Vivo

Objective. While microCT evaluation of atherosclerotic lesions in mice has been formally validated, existing image processing methods remain undisclosed. We aimed to develop and validate a reproducible image processing workflow based on phosphotungstic acid-enhanced microCT scans for the volumetric quantification of atherosclerotic lesions in entire mouse aortas. Approach and Results. 42 WT and 42 apolipoprotein E knockout mouse aortas were scanned. The walls, lumen, and plaque objects were segmented using dual-threshold algorithms. Aortic and plaque volumes were computed by voxel counting and lesion surface by triangulation. The results were validated against manual and histological evaluations. Knockout mice had a significant increase in plaque volume compared to wild types with a plaque to aorta volume ratio of 0.3%, 2.8%, and 9.8% at weeks 13, 18, and 26, respectively. Automatic segmentation correlated with manual ( r 2 ≥ 0.89 ; p < .001 ) and histological evaluations ( r 2 > 0.96 ; p < .001 ). Conclusions. The semiautomatic workflow enabled rapid quantification of atherosclerotic plaques in mice with minimal manual work.

ZFTool: A Software for Automatic Quantification of Cancer Cell Mass Evolution in Zebrafish

Background: Zebrafish (Danio rerio) is a model organism for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient ”casper“ zebrafish line give the capacity to directly observe cancer formation and progression in the living animal. Automatic quantification of cellular proliferation in vivo is critical to the development of personalized medicine. Methods: A new methodology was defined to automatically quantify the cancer cellular evolution. ZFTool was developed to establish a base threshold that eliminates the embryo autofluorescence, automatically measures the area and intensity of GFP (green-fluorescent protein) marked cells, and defines a proliferation index. Results: The proliferation index automatically computed on different targets demonstrates the efficiency of ZFTool to provide a good automatic quantification of cancer cell evolution and dissemination. Conclusion: Our results demonstrate that ZFTool is a reliable tool for the automatic quantification of the proliferation index as a measure of cancer mass evolution in zebrafish, eliminating the influence of its autofluorescence.