Abstract
Tumor segmentation of magnetic resonance 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 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). Each group was trained using different approaches and loss-functions. The output segmentations of a particular label from their respective networks from the three 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 both the BraTS2019 validation and testing datasets. The segmentation networks achieved average dice-scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively on the validation dataset and achieved dice-scores of 0.877, 0.835 and 0.803 for WT, TC and ET respectively on the testing dataset. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244 on the validation dataset and achieved an accuracy score of 0.51 and MSE of 455500 on the testing dataset. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.
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Ostrom, Q.T., Gittleman, H., Truitt, G., Boscia, A., Kruchko, C., Barnholtz-Sloan, J.S.: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro Oncol. 20(suppl_4), iv1–iv86 (2018)
Havaei, M., Davy, A., Warde-Farley, D., et al.: Brain tumor segmentation with Deep Neural Networks. Med. Image Anal. 35, 18–31 (2017)
Louis, D.N., Ohgaki, H., Wiestler, O.D., et al.: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007). https://doi.org/10.1007/s00401-007-0243-4
Kleihues, P., Cavenee, W.K.: Pathology and genetics of tumours of the nervous system, vol 2. International Agency for Research on Cancer (2000)
Lacroix, M., Abi-Said, D., Fourney, D.R., et al.: A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J. Neurosurg. 95(2), 190–198 (2001)
Hakin-Smith, V., Jellinek, D., Levy, D., et al.: Alternative lengthening of telomeres and survival in patients with glioblastoma multiforme. Lancet 361(9360), 836–838 (2003)
Johnson, D.R., O’Neill, B.P.: Glioblastoma survival in the United States before and during the temozolomide era. J. Neurooncol. 107(2), 359–364 (2012)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: MICCAI_BraTS_2018_Proceedings_shortpapers (2018)
Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683–688 (2001)
Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Shreyas, V., Pankajakshan, V.: A deep learning architecture for brain tumor segmentation in MRI images. In: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), Luton, pp. 1–6 (2017)
Pei, L., Reza, S.M.S., Li, W., Davatzikos, C., Iftekharuddin, K.M.: Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc. SPIE Int. Soc. Opt. Eng. 10134 (2017)
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
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16
Funke, J., Martel, J.N., Gerhard, S., et al.: Candidate sampling for neuron reconstruction from anisotropic electron microscopy volumes. Med. Image Comput. Comput. Assist. Interv. 17(Pt 1), 17–24 (2014)
Soeda, A., Hara, A., Kunisada, T., Yoshimura, S.-I., Iwama, T., Park, D.M.: The evidence of glioblastoma heterogeneity. JSR 5, 7979 (2015)
Shboul, Z.A., Vidyaratne, L., Alam, M., Iftekharuddin, K.M.: Glioblastoma and survival prediction. Paper presented at International MICCAI Brainlesion Workshop (2017)
Yang, D., Rao, G., Martinez, J., Veeraraghavan, A., Rao, A.: Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. JMP 42(11), 6725–6735 (2015)
Lee, J., Jain, R., Khalil, K., et al.: Texture feature ratios from relative CBV maps of perfusion MRI are associated with patient survival in glioblastoma. Am. J. Neuroradiol. 37(1), 37–43 (2016)
Sanghani, P., Ang, B.T., King, N.K.K., Ren, H.: Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning. JSO 27(4), 709–714 (2018)
Bakas, S., Akbari, H., Sotiras, A., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data. 4, 170117 (2017)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. https://doi.org/10.17863/CAM.38755
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/k9/tcia.2017.klxwjj1q
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/k9/tcia.2017.gjq7r0ef
Tustison, N.J., Cook, P.A., Klein, A., et al.: Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 99, 166–179 (2014)
Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (2017)
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 29 (2015)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
Wegmayr, V., AS, B.J., Petrick, N., Mori, K. (eds.): Classification of brain MRI with big data and deep 3D convolutional neural networks. In: Published in SPIE Proceedings, Medical Imaging 2018: Computer-Aided Diagnosis, p. 1057501 (2018)
Feng, X., Yang, J., Lipton, Z.C., Small, S.A., Provenzano, F.A.: Deep learning on MRI affirms the prominence of the hippocampal formation in Alzheimer’s disease classification. bioRxiv. 2018; 2018:456277
Van Griethuysen, J.J., Fedorov, A., Parmar, C., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017)
Lee, G.R., Gommers, R., Waselewski, F., Wohlfahrt, K., O’Leary, A.: PyWavelets: a Python package for wavelet analysis. J. Open Source Softw. 4(36), 1237 (2019)
Winger, L.L., Venetsanopoulos, A.N.: Biorthogonal nearly coiflet wavelets for image compression. JSPIC 16(9), 859–869 (2001)
Feng, X., Tustison, N., Meyer, C.: Brain tumor segmentation using an ensemble of 3d U-Nets and overall survival prediction using radiomic features. Paper presented at International MICCAI Brainlesion Workshop (2018)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, pp. 265–284 (2016)
Charles, P.W.D.: Keras. GitHub repository (2013)
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This work was partly supported by the grant, NIH/NCI U01CA207091.
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Bangalore Yogananda, C.G. et al. (2020). Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI. 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_10
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