ESTRO 2023 - Abstract Book

S106

Saturday 13 May

ESTRO 2023

Results Table1 shows the results of segmentation performance comparisons between proposed PW-U-net and the C-U-net for each organ of all test data. The DSC for the 4-PW-U-net, 16-PW-U-net and C-U-net between predicted and ground truth segmentation for prostate were 0.84 ± 0.04, 0.66 ± 0.07 and 0.83 ± 0.04, those for bladder were 0.89 ± 0.03, 0.90 ± 0.02 and 0.86 ± 0.03, those for rectum were 0.79 ± 0.03, 0.65 ± 0.02 and 0.75 ± 0.03, respectively. The HD for prostate were 2.57 ± 0.80, 2.89 ± 0.74 and 2.57 ± 0.81 mm, those for bladder were 2.89 ± 0.91, 2.88 ± 0.55 and 2.95 ± 0.90 mm, those for rectum were 2.16 ± 0.61, 2.46 ± 0.75, 2.26 ± 0.55 mm, respectively. Although the 4-PW-U-net improved the segmentation performance of all organs over the C-U-net, 16-PW-U-net for prostate and rectum did not.

Conclusion In this study, our proposed 4-PW-U-net was demonstrated to improve the performance of the pelvic CT segmentation over the C-U-net. However, our proposed method requires further studies to optimize the patch segmentation and training method for each organ and evaluate the impact of segmentation accuracy on dose determination for both the organ at risk and the target volume.

MO-0144 Pre-clinical use of deep learning optimization improves plan quality in head and neck VMAT A. Arents-Huls 1,1 , M. de Boer 1 , E. van der Wal 1 , D. Fransen 2 , F. Löfman 2 , B. Kreike 1 , R.G. Kierkels 1