Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations.

ABSTRACT:

Purpose

Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a quantitative CT measure of hepatic steatosis, we propose the automatic liver attenuation ROI-based measurement (ALARM) method for automated liver attenuation estimation.

Methods

The ALARM method consists of two major stages: (a) deep convolutional neural network (DCNN)-based liver segmentation and (b) automated ROI extraction. First, liver segmentation was achieved using our previously developed SS-Net. Then, a single central ROI (center-ROI) and three

SUBMITTER: Huo Y 

PROVIDER: S-EPMC6692233 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature