ESTRO 2022 - Abstract Book

S686

Abstract book

ESTRO 2022

We obtained Dice-Sørensen Coefficient (DSC) and Hausdorff distance 95th percentile (HD95) on the external test set with nnUNet’s build-in evaluation function. Test for similarity between the paired models for each OAR was done with a two- sided Wilcoxon signed-rank test with a confidence level of 5 %.

Results DSC and HD95 were significantly better for all OARs with CT+MMD compared to CT-only (p ≤ 0.001, figure 1). Low and no contrast OARs (PCMs and glottic larynx) benefited most, whereas the effect was marginal for the high contrast OAR (parotid glands). Increases in median DSC were 0.56 to 0.86 for glottic larynx, 0.64 to 0.83 for lower PCM, 0.65 to 0.83 for the middle PCM, 0.61 to 0.78 for upper PCM and 0.83 to 0.86 for parotid glands.

Conclusion By training deep learning auto-segmentation models on CT along with just two manually delineated slices per OAR, we demonstrate major improvements of segmentation metrics for low and no contrast OARs such as PCMs and glottic larynx compared to CT-only models. For the included high contrast OAR (parotid glands) CT-only performed well, and no substantial improvements were observed with CT+MMD. Our findings show that it may be beneficial for clinicians to manually delineate a few selected slices of low and no contrast OARs prior to running deep learning auto-segmentation models, as the subsequent predictions are likely to require much less revision than for predictions of fully automated models.

OC-0770 Deep Learning-based segmentation of prostatic urethra on CT scans for treatment planning

L. García-Elcano 1,2 , E. Mylona 3,4 , O. Acosta 3 , T. Lizée 3 , K. Gnep 3 , R. de Crevoisier 3 , J. Pascau 5

1 Universidad Carlos III de Madrid, Departamento de Bioingeniería e Ingeniería Aeroespacial, Madrid, Spain; 2 Centro de Investigación Médica Aplicada, Clínica Universidad de Navarra, Madrid, Spain; 3 Université de Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France; 4 University of Ioannina, Department of Materials Science and