TY  - JOUR
AU  - Langner, Dominik
AU  - Nachbar, Marcel
AU  - Russo, Monica Lo
AU  - Boeke, Simon
AU  - Gani, Cihan
AU  - Niyazi, Maximilian
AU  - Thorwarth, Daniela
TI  - Comparative analysis of open-source against commercial AI-based segmentation models for online adaptive MR-guided radiotherapy.
JO  - Zeitschrift für medizinische Physik
VL  - nn
SN  - 0939-3889
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - DKFZ-2025-00948
SP  - nn
PY  - 2025
N1  - epub
AB  - Online adaptive magnetic resonance-guided radiotherapy (MRgRT) has emerged as a state-of-the-art treatment option for multiple tumour entities, accounting for daily anatomical and tumour volume changes, thus allowing sparing of relevant organs at risk (OARs). However, the annotation of treatment-relevant anatomical structures in context of online plan adaptation remains challenging, often relying on commercial segmentation solutions due to limited availability of clinically validated alternatives. The aim of this study was to investigate whether an open-source artificial intelligence (AI) segmentation network can compete with the annotation accuracy of a commercial solution, both trained on the identical dataset, questioning the need for commercial models in clinical practice.For 47 pelvic patients, T2w MR imaging data acquired on a 1.5 T MR-Linac were manually contoured, identifying prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, and bony structures. These training data were used for the generation of an in-house AI segmentation model, a nnU-Net with residual encoder architecture featuring a streamlined single image inference pipeline, and re-training of a commercial solution. For quantitative evaluation, 20 MR images were contoured by a radiation oncologist, considered as ground truth contours (GTC) and compared with the in-house/commercial AI-based contours (iAIC/cAIC) using Dice Similarity Coefficient (DSC), 95
KW  - Automatic annotation (Other)
KW  - Deep Learning (Other)
KW  - MR-Linac (Other)
KW  - medical image segmentation (Other)
KW  - online adaptive radiotherapy (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40345918
DO  - DOI:10.1016/j.zemedi.2025.04.008
UR  - https://inrepo02.dkfz.de/record/301263
ER  -