Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction
Pages 476 - 484
Abstract
Brain tumor segmentation is a challenging task necessary for quantitative tumor analysis and diagnosis. We apply a multi-scale convolutional neural network based on the DeepMedic to segment glioma subvolumes provided in the 2018 MICCAI Brain Tumor Segmentation Challenge. We go on to extract intensity and shape features from the images and cross-validate machine learning models to predict overall survival. Using only the mean FLAIR intensity, nonenhancing tumor volume, and patient age we are able to predict patient overall survival with reasonable accuracy.
References
[1]
Stupp, R., et al.: The European organisation for research and treatment of cancer brain tumor and radiotherapy groups, and “the national cancer institute of canada clinical trials group”. radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352(10), 987–996 (2005)
[2]
Shipitsin M et al. Molecular definition of breast tumor heterogeneity Cancer cell 2007 11 3 259-273
[3]
Ronneberger O, Fischer P, and Brox T Navab N, Hornegger J, Wells WM, and Frangi AF U-Net: convolutional networks for biomedical image segmentation Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015 2015 Cham Springer 234-241
[4]
Menze BH et al. The multimodal brain tumor image segmentation benchmark (BRATS) IEEE Trans. Med. Imaging 2015 34 10 1993-2024
[5]
Bakas S et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features Sci. Data 2017 4 170117
[6]
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection, The Cancer Imaging Archive (2017).
[7]
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection, The Cancer Imaging Archive (2017).
[8]
Bakas, S., Reyes, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)
[9]
Kamnitsas K et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Med. Image Anal. 2017 36 61-78
[10]
Leung Kelvin K., Clarkson Matthew J., Bartlett Jonathan W., Clegg Shona, Jack Clifford R., Weiner Michael W., Fox Nick C., and Ourselin Sébastien Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection NeuroImage 2010 50 2 516-523
[11]
van Griethuysen Joost J.M., Fedorov Andriy, Parmar Chintan, Hosny Ahmed, Aucoin Nicole, Narayan Vivek, Beets-Tan Regina G.H., Fillion-Robin Jean-Christophe, Pieper Steve, and Aerts Hugo J.W.L. Computational Radiomics System to Decode the Radiographic Phenotype Cancer Research 2017 77 21 e104-e107
[12]
Haralick, R., Shanmugan, K., Dinstein, I.: Textural features for image classification (1973)
[13]
Akaike H A new look at the statistical model identification IEEE Trans. Autom. Control 1974 19 6 716-723
[14]
Kursa MB, Rudnicki WR, et al. Feature selection with the Boruta package J. Stat. Softw. 2010 36 11 1-13
[15]
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. CoRR, abs/1711.01468 (2017)
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Published In
Sep 2018
538 pages
ISBN:978-3-030-11725-2
DOI:10.1007/978-3-030-11726-9
© Springer Nature Switzerland AG 2019.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 26 January 2019
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