Cloud-based semi-automated liver segmentation- Analytical study to compare its speed and accuracy with a semi-automated workstation based software
Poster Presentation at the European Congress of Radiology, Vienna, 2019
We discuss a novel method to semi-automatically segment liver parenchyma and vasculature using deep learning and cloud-based tools.
Methods and Materials
We compared the time taken to segment liver parenchyma between 1) Philips Intellispace, an FDA approved liver segmentation and volumetry software and 2) PredibleLiver, a minimal web-based viewer for medical images and segmentation, with basic editing tools. On the Philips Intellispace software, semi-automated segmentation tools like brush, region-grow, multi-ROI interpolation were used to perform the segmentation. On PredibleLiver, only 3D-brush was used to correct the deep learning initialized segmentations. Time
The deep learning model was trained on 200 tri-phasic CT scans, using a