Establishing Normative Liver Volume and Attenuation for the Population of a Large Developing Country Using Deep Learning – A Retrospective Study of 1,800 CT Scans

Congress:

ECR 2020

Poster Number:

C-07653

Type:

Scientific Exhibit

Keywords:

Artificial Intelligence and Machine Learning, Artificial Intelligence, CT, Computer Applications-Detection, diagnosis, Pathology, Retrospective, Observational, Performed at one institution

Authors:

V. Mahajan¹, V. Venugopal¹, S. Vaidya², A. Chunduru², M. Barnwal¹, S. Gupta¹, H. Mahajan ¹; ¹New Delhi/IN, ²Bangalore/IN

DOI:

10.26044/ecr2020/C-07653

DOI-Link:

https://dx.doi.org/10.26044/ecr2020/C-07653

References

[1] Hepatic Steatosis (Fatty Liver Disease) in Asymptomatic Adults Identified by Unenhanced Low-Dose CT Cody J. Boyce, Perry J. Pickhardt, David H. Kim, Andrew J. Taylor, Thomas C. Winter, Richard J. Bruce, Mary J. Lindstrom, and J. Louis Hinshaw American Journal of Roentgenology 2010 194:3, 623-628

[2] Kodama Y, Ng CS, Wu TT et-al. Comparison of CT methods for determining the fat content of the liver. AJR Am J Roentgenol. 2007;188 (5): 1307-12. doi:10.2214/AJR.06.0992

[3] Lawrence EM, Pooler BD, Pickhardt PJ. Opportunistic Screening for Hereditary Hemochromatosis With Unenhanced CT: Determination of an Optimal Liver Attenuation Threshold. (2018) AJR. American journal of roentgenology. doi:10.2214/AJR.18.19690

[4] Christ, Patrick Ferdinand, et al. "Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2016.