Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation.
Journal:
Computer methods and programs in biomedicine
Published Date:
Nov 4, 2020
Abstract
BACKGROUND AND OBJECTIVE: Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods are difficult to fully automate and thus remain time-consuming. To address these issues, in this study we aim to develop a fully-automated approach for functional region annotation of liver using deep learning based on 2.5D class-aware deep neural networks with spatial adaptation.
