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 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). Results Two hundred and forty‐six 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). From the correlation analyses, the proposed ALARM method achieved Pearson correlations = 0.94 with manual estimation on liver attenuation estimations. When evaluating the ALARM method for detection of nonalcoholic fatty liver disease (NAFLD) using the traditional cut point of < 40 HU, the center‐ROI achieved substantial agreements (Kappa = 0.79) with manual estimation, while the periphery‐ROI method achieved “excellent” agreement (Kappa = 0.88) with manual estimation. The automated ALARM method had reduced variability compared to manual measurements as indicated by a smaller standard deviation. Conclusions We propose a fully automated liver attenuation estimation method termed ALARM by combining DCNN and morphological operations, which achieved “excellent” agreement with manual estimation for fatty liver detection. The entire pipeline is implemented as a Docker container which enables users to achieve liver attenuation estimation in five minutes per CT exam.

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