Abstract
Gliomas are the most common malignant brain tumours with intrinsic heterogeneity. Accurate segmentation of gliomas and their sub-regions on multi-parametric magnetic resonance images (mpMRI) is of great clinical importance, which defines tumour size, shape and appearance and provides abundant information for preoperative diagnosis, treatment planning and survival prediction. Recent developments on deep learning have significantly improved the performance of automated medical image segmentation. In this paper, we compare several state-of-the-art convolutional neural network models for brain tumour image segmentation. Based on the ensembled segmentation, we present a biophysics-guided prognostic model for patient overall survival prediction which outperforms a data-driven radiomics approach. Our method won the second place of the MICCAI 2019 BraTS Challenge for the overall survival prediction.
S. Wang and C. Dai—The two authors contributed equally to this paper.
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Acknowledgement
This work was supported by the SmartHeart EPSRC Programme Grant (EP/P001009/1) and the NIHR Imperial Biomedical Research Centre (BRC).
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Wang, S., Dai, C., Mo, Y., Angelini, E., Guo, Y., Bai, W. (2020). Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_6
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DOI: https://doi.org/10.1007/978-3-030-46643-5_6
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