Automatically Derived Volumetrics for Liver Transplants

Principal author

Beck Olson
Computational and Data Scientist



Beck Olson, a Data Scientist in the Department of Radiology and Biomedical Imaging, is working with Jane Wang, MD, an expert in abdominal imaging, to develop neural networks to automatically derive volumetrics of the liver. UCSF performs approximately 200 liver transplants annually. Preparation for these surgeries historically has involved time consuming semi-automatic segmentation of the donor liver to determine whether there is sufficient organ volume to support positive outcomes for both donor and recipient. With full automation of this segmentation process on the horizon, Olson is now working with NVIDIA on a platform for delivering the results for clinical review.

Automation of this segmentation process
Automation of this segmentation process
Figure 2: Example Results. The green outline is the ground-truth segmentation of the right lobe and the green fill is the result from the trained segmentation model.  The blue outline and fill are the corresponding regions for the left lobe.


Desired clinical outcomes

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