Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring

Peter Jagd Sørensen*, Claes Nøhr Ladefoged, Vibeke Andrée Larsen, Flemming Littrup Andersen, Michael Bachmann Nielsen, Hans Skovgaard Poulsen, Jonathan Frederik Carlsen, Adam Espe Hansen

*Corresponding author for this work

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Abstract

The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm3. Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation.

Original languageEnglish
JournalTomography
Volume10
Issue number9
Pages (from-to)1397-1410
Number of pages14
ISSN2379-1381
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • annotation protocol
  • automatic
  • Brain Tumor Segmentation Challenge
  • brain tumour segmentation
  • BraTS
  • deep learning algorithm
  • magnetic resonance imaging
  • MRI
  • postoperative
  • treatment monitoring

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