Abstract
Accurate assessment of brain tumor progression from magnetic resonance imaging is a critical issue in clinical practice which allows us to precisely monitor the patient’s response to a given treatment. Manual analysis of such imagery is, however, prone to human errors and lacks reproducibility. Therefore, designing automated end-to-end quantitative tumor’s response assessment is of pivotal clinical importance nowadays. In this work, we further investigate this issue and verify the robustness of bidimensional and volumetric tumor’s measurements calculated over the delineations obtained using the state-of-the-art tumor segmentation deep learning model which was ranked 6\(^\textrm{th}\) in the BraTS21 Challenge. Our experimental study, performed over the Brain Tumor Progression dataset, showed that volumetric measurements are more robust against varying-quality tumor segmentation, and that improving brain extraction can notably impact the calculation of the tumor’s characteristics.
This work was supported by the National Centre for Research and Development (POIR.01.01.01-00-0092/20). JN was supported by the Silesian University of Technology funds through the grant for maintaining and developing research potential. This paper is in memory of Dr. Grzegorz Nalepa, an extraordinary scientist, pediatric hematologist/oncologist, and a compassionate champion for kids at Riley Hospital for Children, Indianapolis, USA, who helped countless patients and their families through some of the most challenging moments of their lives.
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Kotowski, K., Machura, B., Nalepa, J. (2023). Robustifying Automatic Assessment of Brain Tumor Progression from MRI. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 13769. Springer, Cham. https://doi.org/10.1007/978-3-031-33842-7_8
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