Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

TL;DR

This paper introduces ALARM, an automated method combining CNN segmentation and morphological operations to measure liver attenuation in CT scans, reducing manual effort and enabling large-scale hepatic steatosis assessment.

Contribution

The study presents a fully automated, open-source pipeline for liver attenuation measurement using deep learning and morphological techniques, improving efficiency over manual methods.

Findings

Achieved accurate liver segmentation with SS-Net.

Validated on 246 external CT scans with consistent results.

Open source implementation available for broader use.

Abstract

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. The ALARM method consists of two major stages: (1) deep convolutional neural network (DCNN)-based liver segmentation and (2) automated ROI extraction. First, liver segmentation was achieved using our previously developed SS-Net. Then, a single central ROI (center-ROI) and three circles ROI (periphery-ROI) were computed based on liver segmentation and morphological operations. The ALARM method is available as an open source Docker container (https://github.com/MASILab/ALARM).246 subjects with 738 abdomen CT scans from the African American-Diabetes Heart Study (AA-DHS) were used for external validation (testing), independent from the training and validation cohort (100 clinically acquired CT abdominal scans).