Hepatic vessel segmentation using a reduced filter 3D U-Net in ultrasound imaging

2019/07/28 by Bart R. Thomson, Thomson, Bart R., Jasper Nijkamp +15 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Advanced Neural Network Applications #FOS: Electrical engineering #Hepatocellular Carcinoma Treatment and Prognosis #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Ultrasound Imaging and Elastography #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1907.12109

openalex publication_date 2019/07/28 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

Abstract

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).

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