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A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12659))

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Abstract

Locating brain tumor and its various sub-regions are crucial for treating tumor in humans. The challenge lies in taking cues for identification of tumors having different size, shape, and location in the brain using multimodal data. Numerous work has been done in the recent past in BRATS challenge [16]. In this work, an ensemble based approach using Deep Random Forest [23] in incremental learning mechanism is deployed. The proposed approach divides data and features into disjoint subsets and learn in chunk as cascading architecture of multi layer RFs. Each layer is also a combination of RFs to use sample of the data to learn diversity present. Given the huge amount of data, the proposed approach is fast and paralleled. In addition, we have proposed new kind of Local Binary Pattern (LBP) features with rotation. Also, few more handcrafted are designed primarily texture based features, appearance based features, statistical based features. The experiments are performed only on MICCAI BRATS 2020 dataset.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Vadodara, Gandhinagar, 382028, Gujarat, India

    Sameer Shaikh & Ashish Phophalia

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  1. Sameer Shaikh
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  2. Ashish Phophalia
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Correspondence to Ashish Phophalia .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Shaikh, S., Phophalia, A. (2021). A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-72087-2_12

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  • Print ISBN: 978-3-030-72086-5

  • Online ISBN: 978-3-030-72087-2

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