{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:20:18Z","timestamp":1770816018368,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy"},{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["323339"],"award-info":[{"award-number":["323339"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MiDT"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Quantification of the residual tumor from early post-operative magnetic resonance imaging (MRI) is essential in follow-up and treatment planning for glioblastoma patients. Residual tumor segmentation from early post-operative MRI is particularly challenging compared to the closely related task of pre-operative segmentation, as the tumor lesions are small, fragmented, and easily confounded with noise in the resection cavity. Recently, several studies successfully trained deep learning models for early post-operative segmentation, yet with subpar performances compared to the analogous task pre-operatively. In this study, the impact of image and annotation quality on model training and performance in early post-operative glioblastoma segmentation was assessed. A dataset consisting of early post-operative MRI scans from 423 patients and two hospitals in Norway and Sweden was assembled, for which image and annotation qualities were evaluated by expert neurosurgeons. The Attention U-Net architecture was trained with five-fold cross-validation on different quality-based subsets of the dataset in order to evaluate the impact of training data quality on model performance. Including low-quality images in the training set did not deteriorate performance on high-quality images. However, models trained on exclusively high-quality images did not generalize to low-quality images. Models trained on exclusively high-quality annotations reached the same performance level as the models trained on the entire dataset, using only two-thirds of the dataset. Both image and annotation quality had a significant impact on model performance. In dataset curation, images should ideally be representative of the quality variations in the real-world clinical scenario, and efforts should be made to ensure exact ground truth annotations of high quality.<\/jats:p>","DOI":"10.3390\/jimaging12020073","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T09:16:08Z","timestamp":1770801368000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Impact of Data Quality in Early Post-Operative Glioblastoma Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-4876","authenticated-orcid":false,"given":"Ragnhild Holden","family":"Helland","sequence":"first","affiliation":[{"name":"Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway"},{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5669-9514","authenticated-orcid":false,"given":"David","family":"Bouget","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2860-9331","authenticated-orcid":false,"given":"Asgeir Store","family":"Jakola","sequence":"additional","affiliation":[{"name":"Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden"},{"name":"Department of Neurosurgery, Sahlgrenska University Hospital, 40530 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9084-0991","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Muller","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5954-4817","authenticated-orcid":false,"given":"Ole","family":"Solheim","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, NO-7030 Trondheim, Norway"},{"name":"Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0999-3849","authenticated-orcid":false,"given":"Ingerid","family":"Reinertsen","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway"},{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Grochans, S., Cybulska, A.M., Simi\u0144ska, D., Korbecki, J., Kojder, K., Chlubek, D., and Baranowska-Bosiacka, I. 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