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Introduction: Accurate and reproducible scar quantification of late gadolinium enhancement (LGE) images from cardiac magnetic resonance imaging (CMR) is important in risk stratifying hypertrophic cardiomyopathy (HCM) patients. Previous machine learning algorithms for CMR LGE quantification deployed three-dimensional convolutional neural network (CNN) architecture, which required image cropping and custom graphic processing units (GPUs) to function, thus limiting their general applicability. We aim to develop a deep two-dimensional (2D) CNN model that contours the left ventricle (LV) endo- and epicardial borders and quantifies LGE. Hypothesis: We hypothesize that a deep 2D CNN model, which uses commercially available GPUs, could be used to efficiently and accurately contour LV endo- and epicardial borders and quantify CMR LGE in HCM patients. Methods: We retrospectively studied 296 HCM patients (2423 images) from the University Health Network (Toronto, Canada) and Tufts Medical Center (Boston, USA). LGE images were manually segmented by an expert reader. Scar was defined as 5 standard deviations higher than the mean of the annotated normal region pixels. A 2D U-net CNN variant was used to train a model on 80% of the datasets. Testing was performed on the remaining 20%. We applied a 5-folds cross validation algorithm for training to improve model robustness. Model performance was assessed using the Dice Similarity Coefficient (DSC). Results: We were able to develop a deep learning model that could successfully perform both LV segmentation and scar quantification using a generally available GPU card. Our algorithm did not require image cropping and processed one image every 60 milliseconds. DSC scores averaged across the 5-folds was excellent at 0.89+0.22 for the endocardium and 0.81+0.17 for the epicardium, and good at 0.57+0.31 for scar. Conclusions: Using novel 2D CNN methods, we have successfully developed an automatic algorithm that rapidly provides LV endo- and epicardial contours and scar quantification on LGE CMR images that is superior to previously published studies. Unlike previous algorithms, our program does not require the use of custom CPUs or image cropping, potentially allowing it to be integrated into routine clinical practice.

Single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates scATAC-seq count matrices that highly resemble real scATAC-seq datasets in library size, sparsity, and chromatin accessibility signals. simATAC deploys statistical models derived from analyzing 90 real scATAC-seq cell groups. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with known cell labels for assessing analytical pipelines.