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.
C. Dai and S. Wang—Contributed equally.
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References
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The cancer imaging archive (2017)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BraTS Challenge. arXiv:1811.02629 (2018)
Baldock, A.L., Ahn, S., Rockne, R., Johnston, S., Neal, M., et al.: Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas. PLoS One 9(10), e99057 (2014)
Hanif, F., Muzaffar, K., Perveen, K., Malhi, S.M., Simjee, S.U.: Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pac. J. Cancer Prev. APJCP 18(1), 3 (2017)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018, Part II. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019, Part I. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_22
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38
Li, C., Wang, S., Liu, P., Torheim, T., Boonzaier, N.R., et al.: Decoding the interdependence of multiparametric magnetic resonance imaging to reveal patient subgroups correlated with survivals. Neoplasia 21(5), 442–449 (2019)
Li, C., et al.: Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur. Radiol. 29(9), 4718–4729 (2019). https://doi.org/10.1007/s00330-018-5984-z
Li, C., et al.: Intratumoral heterogeneity of glioblastoma infiltration revealed by joint histogram analysis of diffusion tensor imaging. Neurosurgery 85, 524–534 (2018)
Li, C., Wang, S., Yan, J.L., Torheim, T., Boonzaier, N.R., et al.: Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging. J. Neurosurg. 1, 1–8 (2019)
Mang, A., Bakas, S., Subramanian, S., Davatzikos, C., Biros, G.: Integrated biophysical modeling and image analysis: application to neuro-oncology. Annu. Rev. Biomed. Eng. 22, 309–341 (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018, Part II. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197 (2019)
Scialpi, M., Bianconi, F., Cantisani, V., Palumbo, B.: Radiomic machine learning: is it really a useful method for the characterization of prostate cancer? Radiology 291(1), 269 (2019)
Wang, S., Dai, C., Mo, Y., Angelini, E., Guo, Y., Bai, W.: Automatic brain tumour segmentation and biophysics-guided survival prediction. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019, Part II. LNCS, vol. 11993, pp. 61–72. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46643-5_6
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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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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