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Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12658)

Abstract

Gliomas are among the most common types of malignant brain tumours in adults. Given the intrinsic heterogeneity of gliomas, the multi-parametric magnetic resonance imaging (mpMRI) is the most effective technique for characterising gliomas and their sub-regions. Accurate segmentation of the tumour sub-regions on mpMRI is of clinical significance, which provides valuable information for treatment planning and survival prediction. Thanks to the recent developments on deep learning, the accuracy of automated medical image segmentation has improved significantly. In this paper, we leverage the widely used attention and self-training techniques to conduct reliable brain tumour segmentation and uncertainty estimation. Based on the segmentation result, we present a biophysics-guided prognostic model for the prediction of overall survival. Our method of uncertainty estimation has won the second place of the MICCAI 2020 BraTS Challenge.

Keywords

  • Brain imaging
  • Deep learning
  • Tumour segmentation
  • Radiomics

C. Dai and S. Wang—Contributed equally.

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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). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this challenge.

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Correspondence to Chengliang Dai or Shuo Wang .

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Dai, C. et al. (2021). Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_46

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  • DOI: https://doi.org/10.1007/978-3-030-72084-1_46

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