Author:
Zhang Zhongyi,Li Guixia,Wang Ziqiang,Xia Feng,Zhao Ning,Nie Huibin,Ye Zezhong,Lin Joshua S.,Hui Yiyi,Liu Xiangchun
Abstract
AbstractUnenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.
Funder
Foundation of Shenzhen Third People’s Hospital
Hainan Provincial Natural Science Foundation of China
The Second Hospital of Shandong University
Publisher
Springer Science and Business Media LLC
Reference55 articles.
1. Nassir, F., Rector, R. S., Hammoud, G. M. & Ibdah, J. A. Pathogenesis and prevention of hepatic steatosis. Gastroenterol. Hepatol. 11, 167–175 (2015).
2. Dam-Larsen, S. et al. Long term prognosis of fatty liver: risk of chronic liver disease and death. Gut 53, 750–755 (2004).
3. Bravo, A. A., Sheth, S. G. & Chopra, S. Liver biopsy. N. Engl. J. Med. 344, 495–500 (2001).
4. European Association for the Study of the Liver. Electronic address: easloffice@easloffice.eu, Clinical Practice Guideline Panel, Chair: EASL Governing Board representative: & Panel members: EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 2021 update. J. Hepatol. 75, 659–689 (2021).
5. Idilman, I. S., Ozdeniz, I. & Karcaaltincaba, M. Hepatic steatosis: Etiology, patterns, and quantification. Semin. Ultrasound CT MR 37, 501–510 (2016).