AM
Adriënne Mendrik
4 records found
1
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem as it makes training data heterogeneous but so far no attempts have been made to explicitly capture t
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Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-net) that extracts acquisition-invariant feature vectors. These
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We introduce a novel surrogate-assisted Genetic Algorithm (GA) for expensive optimization of problems with discrete categorical variables. Specifically, we leverage the strengths of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), a state-of-the-art GA, and, for the f
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Background: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography (CT) scans.
Method: The ...
Method: The ...