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Found 4 papers, 2 papers with code
$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters
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.
2.5D Thermometry Maps for MRI-guided Tumor Ablation
For reconstruction, we use a weighted interpolation on a cylindric coordinate representation to calculate the heat value of voxels in a region of interest.
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
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 in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours.
