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ECR 2019 / C-3419
Machine Learning-Assisted Prediction of Hepatic Steatosis using 3D-CNN Auto-segmentation of Contrast-enhanced Portal Venous Phase CT Examinations of the Abdomen
Congress: ECR 2019
Poster No.: C-3419
Type: Scientific Exhibit
Keywords: Cirrhosis, Segmentation, Computer Applications-Detection, diagnosis, CT, Liver, Artificial Intelligence, Abdomen
Authors: S. L. Mihalcioiu1, R. Remtulla2, O. ciga2, C. H. MO3, M. D. A. Attarian3, P. Savadjiev3, S. BHATNAGAR3, C. Reinhold1, J. J. R. Chong1; 1Montreal, QC/CA, 2Montreal, Quebec/CA, 3Montreal/CA
DOI:10.26044/ecr2019/C-3419

Results

Evaluation of 3D-CNN segmentation on the held-out local Test set yielded a Mean Average Error (MAE) of 2.55 HU for the auto-segmented Median PVP density compared to the manual ROI PVP density (Fig. 3). When evaluating the True versus the Simulated Plain Density on the Training dataset, the linear regression model demonstrated a R2 of 0.873, RMSE of 6.979, and MAE of 5.82HU. The full end-to-end algorithm was evaluated on the 40 Test cases, which yielded a Simulated Average Plain Density MAE of 4.78HU.

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