Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

1 code implementation • 20 Jul 2022 • Hana Haseljić, Soumick Chatterjee, Robert Frysch, Vojtěch Kulvait, Vladimir Semshchikov, Bennet Hensen, Frank Wacker, Inga Brüsch, Thomas Werncke, Oliver Speck, Andreas Nürnberger, Georg Rose

This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.

Liver Segmentation

2.5D Thermometry Maps for MRI-guided Tumor Ablation

1 code implementation • 12 Aug 2021 • Julian Alpers, Daniel L. Reimert, Maximilian Rötzer, Thomas Gerlach, Marcel Gutberlet, Frank Wacker, Bennet Hensen, Christian Hansen

For reconstruction, we use a weighted interpolation on a cylindric coordinate representation to calculate the heat value of voxels in a region of interest.

$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters

no code implementations • 14 Jun 2017 • Hinrich B Winther, Christian Hundt, Bertil Schmidt, Christoph Czerner, Johann Bauersachs, Frank Wacker, Jens Vogel-Claussen

Conclusions: Biventricular mass and function parameters can be determined reliably in high quality by applying a deep neural network for cardiac MRI segmentation, especially in the anatomically complex right ventricle.

Anatomy MRI segmentation +1