ESTRO 2023 - Abstract Book

S1346

Digital Posters

ESTRO 2023

Conclusion This work demonstrates that previously trained autocontouring models show a consistent level of accuracy when applied to a previously unseen dataset from a novel clinical centre. Hence, the potential has been demonstrated for these previously trained models to be clinically viable in the local centre. This would reduce the resource demand associated with contouring and reduce variability in patient treatment which results from manual contouring. Limitations include available computational resources forcing a patch-based approach to classification, which may degrade the quality of output contours. Discrepancies between manual and autocontours tended to occur in the superior and inferior extent of tubular structures or at structure interfaces, as previously reported; or where local guidelines differ from national guidelines, which has caused preliminary exclusion of some structures from this analysis.

Further work will include a prospective assessment of time savings delivered by autocontouring.

PO-1648 Deep Learning-Based Automatic Segmentation for Brain OARs: Accuracy and Dosimetric Impact

A. Turcas 1,2,3,4 , C. Gheara 2,5 , D. Leucuta 6 , C. Balan 2,7 , C. Dana 2