## 5 Conclusions  
A deep learning-based (nnU-Net) model for the automatic segmentation of the ventricles and PVS according to the EPTN 2021 guidelines was developed and validated. The model showed good performance across both internal and external test sets, with segmentation results falling within the interobserver delineation variability. On the internal dataset, the nnU-Net model surpassed an openly available off-the-shelf model (SynthSeg) in terms of both the segmentation metrics and clinical acceptability scores. In the external test set, while no significant differences in the segmentation metrics were observed between the two models, the nnU-Net model received significantly higher clinician ratings, indicating a potential advantage in the clinical acceptability of the ventricle segmentations. Therefore, training a model from scratch may be considered if there is a need for specialized fine-tuning to address institution-specific data characteristics. Otherwise, selecting an existing pretrained model would likely yield comparable outcomes, making it a practical and efficient alternative. The developed nnU-Net model, made publicly available on Cancerdata.org and GitLab, holds promise for improving radiotherapy planning workflows by reducing manual segmentation efforts and could also support future research in predicting treatment-related side effects.  
**Author Contributions:** Conceptualization, M.W., J.M.W., W.v.E., I.C., D.B.P.E. and C.M.L.Z.; Data curation, M.W., D.H., N.E.B., K.P., H.L.v.d.W., C.L.B., M.C.A.K. and D.B.P.E.; Formal analysis, M.W., M.R., F.V., H.H.G.H. and C.M.L.Z.; Investigation, M.W., M.R., I.C., D.B.P.E. and C.M.L.Z.; Methodology, M.W., J.M.W., W.v.E., I.C., D.B.P.E. and C.M.L.Z.; Supervision, J.M.W., W.v.E., D.B.P.E. and C.M.L.Z.; Validation, M.W.; Visualization, M.W. and H.H.G.H.; Writing--original draft, M.W.; Writing--review and editing, M.W., M.R., J.M.W., W.v.E., I.C., D.H., N.E.B., F.V., K.P., H.H.G.H., H.L.v.d.W., C.L.B., M.C.A.K., D.B.P.E. and C.M.L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication is part of the project \"Making radiotherapy sustainable\" with project number 10070012010002 of the Highly Specialised Care & Research programme (TZO programme), which is (partly) financed by the Netherlands Organisation for Health Research and Development (ZonMw).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of MAASTRO (protocol code P0632 and date of approval: 24 April 2023).  
**Informed Consent Statement:** The internal review board of MAASTRO reviewed the study and waived the requirement to obtain informed consent given the retrospective nature of the study and the use of non-identifiable data.  
**Data Availability Statement:** Original data from this manuscript will be made available upon reasonable request. The developed DL model will be made available on CancerData.org and can already be accessed via GitHub at [[[https://gitlab.com/ventricle_segmentation/nunnet-ventricle-segmentation](https://gitlab.com/ventricle_segmentation/nunnet-ventricle-segmentation)]([https://gitlab.com/ventricle_segmentation/nunnet-ventricle-segmentation](https://gitlab.com/ventricle_segmentation/nunnet-ventricle-segmentation))] (accessed on 4 May 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.