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Figure 1

From: Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

Figure 1

(A) Overall workflow of the study. (B) Architecture for 3D-patch based U-Net with attention mechanism (C) nnU-Net framework which automatically optimizes the architecture based on the type of datasets. *Quantitative analysis were performed by calculating Dice similarity coefficient, 95th percentile Hausdoff’s distance, and percent change in the volume of segments and spleen between AI predicted and ground-truth contours. Statistical analysis was performed using Wilcoxon signed rank test with Bonferroni correction. **All models were assessed on cohorts of Block 3 using both quantitative and qualitative analyses (Figures created using biorender.com).

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