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International MICCAI Brainlesion Workshop

BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 369–379Cite as

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

Part of the Lecture Notes in Computer Science book series (LNIP,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

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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.

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Authors and Affiliations

  1. Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

    Ujjwal Baid, Sanjay Talbar & Siddhesh Thakur

  2. Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India

    Swapnil Rane, Sudeep Gupta, Meenakshi H. Thakur, Aliasgar Moiyadi & Abhishek Mahajan

Authors
  1. Ujjwal Baid
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  2. Sanjay Talbar
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  3. Swapnil Rane
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  4. Sudeep Gupta
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  5. Meenakshi H. Thakur
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  6. Aliasgar Moiyadi
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  7. Siddhesh Thakur
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  8. Abhishek Mahajan
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Corresponding author

Correspondence to Abhishek Mahajan .

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Editors and Affiliations

  1. University Hospital of Zurich, Zürich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. National Cancer Institute, Bethesda, MD, USA

    Farahani Keyvan

  5. University of Bern, Bern, Switzerland

    Mauricio Reyes

  6. Erasmus University Medical Center, Rotterdam, The Netherlands

    Theo van Walsum

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Baid, U. et al. (2019). Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-11726-9_33

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