Advertisement

Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas

  • Ujjwal Baid
  • Sanjay Talbar
  • Swapnil Rane
  • Sudeep Gupta
  • Meenakshi H. Thakur
  • Aliasgar Moiyadi
  • Siddhesh Thakur
  • Abhishek MahajanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Automated segmentation of brain tumors in multi-channel Magnetic Resonance Image (MRI) is a challenging task. Heterogeneous appearance of brain tumors in MRI poses critical challenges in diagnosis, prognosis and survival prediction. In this paper, we present a novel approach for glioma tumor segmentation and survival prediction with Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model using 3D patch based U-Net model in Brain Tumor Segmentation (BraTS) challenge 2018. Radiomics feature extraction and classification was done on segmented tumor for overall survival (OS) prediction task. Preliminary results of DRAG model on BraTS 2018 validation dataset demonstrated that the proposed method achieved a good performance with Dice scores as 0.88, 0.83 and 0.75 for whole tumor, tumor core and enhancing tumor, respectively. For survival prediction, 57.1% accuracy was achieved on the validation dataset. The proposed DRAG model was one of the top performing models and accomplished third place for OS prediction task in BraTS 2018 challenge.

Keywords

Brain Tumor Segmentation Gliomas Convolutional Neural Networks Radiomics MRI Radiogenomics Survival prediction 

Notes

Acknowledgement

This work was supported by Ministry of Electronics and Information Technology, Govt. of India under Visvesvaraya PhD scheme with implementation reference number: PhD-MLA/4(67/2015-16). Authors are thankful to Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded and Tata Memorial Centre, Mumbai.

References

  1. 1.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection (2017)Google Scholar
  2. 2.
    Bakas, S. et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection (2017)Google Scholar
  3. 3.
    Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 1–13 (2017)CrossRefGoogle Scholar
  4. 4.
    Kickingereder, P., et al.: Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiol. 280(3), 880–889 (2016)CrossRefGoogle Scholar
  5. 5.
    Konstantinos, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  6. 6.
    Mahajan, A., et al.: Bench to bedside molecular functional imaging in translational cancer medicine: to image or to imagine? Clin. Radiol. 70(10), 1060–1082 (2015)CrossRefGoogle Scholar
  7. 7.
    Mahajan, A., Moiyadi, A.V., Jalali, R., Sridhar, E.: Radiogenomics of glioblastoma: a window into its imaging and molecular variability. Cancer Imaging 15(Suppl. 1), 5–7 (2015)Google Scholar
  8. 8.
    Martin, A., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. CoRR (2016)Google Scholar
  9. 9.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BraTS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  10. 10.
    Nelly, G., Eduard, M., Pilar, S.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)CrossRefGoogle Scholar
  11. 11.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  12. 12.
    Seow, P., Wong, J.H.D., Ahmad-Annuar, A., Mahajan, A., Abdullah, N.A., Ramli, N.: Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br. J. Radiol. 91, 20170930 (2017)CrossRefGoogle Scholar
  13. 13.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  14. 14.
    Baid, U., Talbar, S., Talbar, S.: Brain tumor segmentation based on non negative matrix factorization and fuzzy clustering. In: Fifth International Conference on Bio-Imaging (2017)Google Scholar
  15. 15.
    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)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ujjwal Baid
    • 1
  • Sanjay Talbar
    • 1
  • Swapnil Rane
    • 2
  • Sudeep Gupta
    • 2
  • Meenakshi H. Thakur
    • 2
  • Aliasgar Moiyadi
    • 2
  • Siddhesh Thakur
    • 1
  • Abhishek Mahajan
    • 2
    Email author
  1. 1.Shri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia
  2. 2.Tata Memorial Hospital, Tata Memorial CentreMumbaiIndia

Personalised recommendations