  Accurate hepatic vessel segmentation on ultrasound (US) images can be an
important tool in the planning and execution of surgery, however proves to be a
challenging task due to noise and speckle. Our method comprises a reduced
filter 3D U-Net implementation to automatically detect hepatic vasculature in
3D US volumes. A comparison is made between volumes acquired with a 3D probe
and stacked 2D US images based on electromagnetic tracking. Experiments are
conducted on 67 scans, where 45 are used in training, 12 in validation and 10
in testing. This network architecture yields Dice scores of 0.740 and 0.781 for
3D and stacked 2D volumes respectively, comparing promising to literature and
inter-observer performance (Dice = 0.879).
