Abstract #3592

Improving Early Post-Operative Glioblastoma Segmentation With Semi-Supervised Deep Learning

Lidia Luque^1,2,3, Karoline Skogen⁴, Bradley J MacIntosh^3,5,6, Kyrre Eeg Emblem¹, Christopher Larsson^3,7, Einar O Vik-Mo⁷, and Atle Bjørnerud^1,3

¹Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway, ²Department of Physics, University of Oslo, Oslo, Norway, ³Computational Radiology and Artificial Intelligence (CRAI), Oslo University Hospital, Oslo, Norway, ⁴Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, ⁵Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, ⁶Sandra E Black Centre for Brain Resilience and Recovery, Sunnybrook Research Institute, Toronto, ON, Canada, ⁷Department of Neurosurgery, Oslo University Hospital, Oslo, Norway

Synopsis

Keywords: Segmentation, Machine Learning/Artificial Intelligence, Semi-supervisionImproving automatic segmentation of glioblastoma on early post-operative MRI is key to study the effect of resection volumes on patient outcomes. We curate a dataset of over 700 MRI examinations, of which 87 include annotations, and train a supervised and a semi-supervised deep-learning model. Semi-supervision improves the segmentation of the high-intensity FLAIR signal with 3% to a Dice score of 0.83 (p=0.031), while the segmentation of the enhancing tumor increases with 9% to 0.55 (p=0.056). However, enhancing tumor segmentations show high variability, possibly due to imperfect annotations. Segmentation of enhancing tumor on early post-operative MRI remains a challenging task.

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