Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

[1]  Vinod Pankajakshan,et al.  A deep learning architecture for brain tumor segmentation in MRI images , 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).

[2]  Z L Gokaslan,et al.  A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. , 2001, Journal of neurosurgery.

[3]  J. Royds,et al.  Alternative lengthening of telomeres and survival in patients with glioblastoma multiforme , 2003, The Lancet.

[4]  R. Barnard.  The classification of tumours of the central nervous system. , 1982, Neuropathology and applied neurobiology.

[5]  Allan Hanbury,et al.  Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.

[6]  Konstantinos Kamnitsas,et al.  Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.

[7]  B. O'neill,et al.  Glioblastoma survival in the United States before and during the temozolomide era , 2012, Journal of Neuro-Oncology.

[8]  Sébastien Ourselin,et al.  Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks , 2017, BrainLes@MICCAI.

[9]  Arno Klein,et al.  Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements , 2014, NeuroImage.

[10]  et al.,et al.  Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.

[11]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[12]  Christos Davatzikos,et al.  Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI , 2017, Medical Imaging.

[13]  Deric M. Park,et al.  The Evidence of Glioblastoma Heterogeneity , 2015, Scientific Reports.

[14]  Luca Maria Gambardella,et al.  Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes , 2014, MICCAI.

[15]  Khan M. Iftekharuddin,et al.  Glioblastoma and Survival Prediction , 2017, BrainLes@MICCAI.

[16]  Christos Davatzikos,et al.  Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.

[17]  E. Holland.  Progenitor cells and glioma formation , 2001, Current opinion in neurology.

[18]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[19]  Jie Yang,et al.  Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification , 2018, bioRxiv.

[20]  B. Ang,et al.  Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning. , 2018, Surgical oncology.

[21]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[22]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Juan J. Martinez,et al.  Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. , 2015, Medical physics.

[24]  Jill S Barnholtz-Sloan,et al.  CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015. , 2018, Neuro-oncology.

[25]  Andriy Myronenko,et al.  3D MRI brain tumor segmentation using autoencoder regularization , 2018, BrainLes@MICCAI.

[26]  Anastasios N. Venetsanopoulos,et al.  Biorthogonal nearly coiflet wavelets for image compression , 2001, Signal Process. Image Commun..

[27]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[28]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[29]  M. Brada.  Pathology and Genetics of Tumours of the Nervous System , 2001, British Journal of Cancer.

[30]  Nicholas J. Tustison,et al.  Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features , 2018, Frontiers in Computational Neuroscience.

[31]  Joachim M. Buhmann,et al.  Classification of brain MRI with big data and deep 3D convolutional neural networks , 2018, Medical Imaging.

[32]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

[33]  Aaron O'Leary,et al.  PyWavelets: A Python package for wavelet analysis , 2019, J. Open Source Softw..

[34]  A. Rao,et al.  Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma , 2016, American Journal of Neuroradiology.

[35]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.