Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA.
Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37235, USA.
Med Phys. 2019 Aug;46(8):3508-3519. doi: 10.1002/mp.13675. Epub 2019 Jul 5.
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: (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).
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.
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.
手动追踪肝脏感兴趣区域 (ROI) 是在计算机断层扫描 (CT) 上诊断非酒精性脂肪性肝病 (NAFLD) 时测量肝脏衰减的实际标准方法。然而，手动追踪需要大量资源。为了解决这些限制并扩展肝脂肪变性的定量 CT 测量的可用性，我们提出了基于自动肝脏衰减 ROI 的测量 (ALARM) 方法，用于自动估计肝脏衰减。
ALARM 方法由两个主要阶段组成：(a) 基于深度卷积神经网络 (DCNN) 的肝脏分割和 (b) 自动 ROI 提取。首先，使用我们之前开发的 SS-Net 实现肝脏分割。然后，根据肝脏分割和形态学操作计算单个中央 ROI（中心 ROI）和三个圆形 ROI（外周 ROI）。ALARM 方法可作为开源 Docker 容器使用（https://github.com/MASILab/ALARM）。
来自非裔美国人-糖尿病心脏研究 (AA-DHS) 的 246 名患者和 738 例腹部 CT 扫描用于外部验证（测试），与训练和验证队列（100 例临床获得的腹部 CT 扫描）独立。从相关分析中，所提出的 ALARM 方法与手动估计肝脏衰减值的 Pearson 相关系数为 0.94。当使用<40 HU 的传统临界值评估 ALARM 方法对非酒精性脂肪性肝病 (NAFLD) 的检测时，中心 ROI 与手动估计具有实质性一致性（Kappa=0.79），而外周 ROI 方法与手动估计具有“优秀”一致性（Kappa=0.88）。与手动测量相比，自动 ALARM 方法的变异性降低，这表明标准差较小。
我们提出了一种全自动肝脏衰减估计方法，称为 ALARM，它通过结合 DCNN 和形态学操作实现了对脂肪肝检测的“优秀”一致性。整个流水线实现为 Docker 容器，用户可以在每个 CT 检查中在五分钟内实现肝脏衰减估计。
