Nikolas Lessmann

TL;DR: The presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening and is evaluated on a set of 1744 CT scans from the National Lung Screening Trial.

TL;DR: An iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebraes is proposed and compares favorably with state‐of‐the‐art methods.

TL;DR: The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis, and may potentially reduce the number of patients undergoing unnecessary invasive FFR measurements.

TL;DR: A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population.

TL;DR: The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations.