Titlebook: Bildverarbeitung für die Medizin 2022; Proceedings, German Klaus Maier-Hein,Thomas M. Deserno,Thomas Tolxdorf Conference proceedings 2022

disparage

Robust Liver Segmentation with Deep Learning Across DCE-MRI Contrast Phases,nhanced MRI is particularly relevant. Previouswork has focused on liver segmentation in the late hepatobiliary contrast phase, which may not always be available in heterogeneous data from clinical routine. In this contribution, we demonstrate the training of a convolutional neural network across con

opalescence

animated

NOMAD

Unsupervised Anomaly Detection in the Wild,chniques without the need for explicitly labeled data. However, most previous works study different methods in a constrained research setting with a limited number of common types of pathologies. Here, we want to explore a more realistic setting and target the incidental findings in a large-scale po

Instrumental

novelty

BULLY

Detection of Large Vessel Occlusions Using Deep Learning by Deforming Vessel Tree Segmentations,ment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of

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Machine Learning-based Detection of Spherical Markers in CT Volumes,s of the markers is crucial for an accurate alignment. A typical approach utilizes a 3D version of fast radial symmetry transform for marker detection. This method works only for a given set of radii and tends to be influenced by reconstruction artifacts.With a desire for a more robust solution, a d

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