Publications (16)
Random smooth gray value transformations for cross modality learning with gray value invariant networks
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. These transformations normally aim…
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Alessa Hering, Stephanie Häger, Jan Moltz +3
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance…
Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network
Nikolas Lessmann, Jelmer M. Wolterink, Majd Zreik +3
Thin-plate splines can be used for interpolation of image values, but can also be used to represent a smooth surface, such as the boundary between two structures. We present a meth…
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
Majd Zreik, Nikolas Lessmann, Robbert W. van Hamersvelt +5
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during…
Semi-Supervised Segmentation via Embedding Matching
Weiyi Xie, Nathalie Willems, Nikolas Lessmann +2
Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time…