Search Results for author: Nikolas Lessmann

Found 13 papers, 4 papers with code

Kidney abnormality segmentation in thorax-abdomen CT scans

no code implementations6 Sep 2023 Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten, Mathias Prokop, Colin Jacobs, Bram van Ginneken

Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.

Lumbar spine segmentation in MR images: a dataset and a public benchmark

1 code implementation21 Jun 2023 Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens, Matthieu Rutten, Job L. C. van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, Nikolas Lessmann

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal.

Random smooth gray value transformations for cross modality learning with gray value invariant networks

1 code implementation MIDL 2019 Nikolas Lessmann, Bram van Ginneken

Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples.

Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT

no code implementations12 Feb 2019 Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Pim A. de Jong, Nikolas Lessmann, Ivana Isgum

To meet demands of the increasing interest in quantification of CAC, i. e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed.

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

1 code implementation12 Apr 2018 Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Išgum

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.

Instance Segmentation Semantic Segmentation

Direct and Real-Time Cardiovascular Risk Prediction

no code implementations8 Dec 2017 Bob D. de Vos, Nikolas Lessmann, Pim A. de Jong, Max A. Viergever, Ivana Isgum

The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

no code implementations1 Nov 2017 Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum

On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.

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