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International Workshop on Machine Learning in Clinical Neuroimaging

International Workshop on Radiomics and Radiogenomics in Neuro-oncology

MLCN 2020, RNO-AI 2020: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology pp 189–198Cite as

State-of-the-Art in Brain Tumor Segmentation and Current Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12449))

Abstract

Brain tumors are the third most common type of cancer among young adults and an accurate diagnosis and treatment demands strict delineation of the tumor effected tissue. Brain tumor segmentation involves segmenting different tumor tissues, particularly, the enhancing tumor regions, non-enhancing tumor and necrotic regions, and edema. With increasing computational power and data sharing, computer vision algorithms, particularly deep learning approaches, have begun to dominate the field of medical image segmentation. Accurate tumor segmentation will help in surgery planning as well as monitor the progress in longitudinal studies enabling a better understanding of the factors effecting malignant growth. The objective of this paper is to provide an overview of the current state-of-the-art in brain tumor segmentation approaches, an idea of the available resources, and highlight the most promising research directions moving forward. We also intend to highlight the challenges that exist in this field, in particular towards the successful adoption of such methods to clinical practice.

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Notes

  1. 1.

    https://www.cancerimagingarchive.net.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, UET Taxila, Taxila, Pakistan

    Sobia Yousaf, Syed Muhammad Anwar & Nosheen Sohail

  2. CRCV, University of Central Florida, Orlando, FL, USA

    Harish RaviPrakash & Ulas Bagci

Authors
  1. Sobia Yousaf
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  2. Harish RaviPrakash
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  3. Syed Muhammad Anwar
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  4. Nosheen Sohail
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  5. Ulas Bagci
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Corresponding author

Correspondence to Syed Muhammad Anwar .

Editor information

Editors and Affiliations

  1. Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  2. Lahore University of Management Sciences, Lahore, Pakistan

    Hassan Mohy-ud-Din

  3. University of Pennsylvania, Philadelphia, PA, USA

    Ahmed Abdulkadir

  4. King’s College London, London, UK

    Cher Bass

  5. The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Mohamad Habes

  6. University College London, London, UK

    Jane Maryam Rondina

  7. CUBRIC, Cardiff, UK

    Chantal Tax

  8. IBM Almaden Research Center, San Jose, CA, USA

    Hongzhi Wang

  9. University of Oslo, Oslo, Norway

    Thomas Wolfers

  10. Eli Lilly Pharmaceutical Company, Philadelphia, PA, USA

    Saima Rathore

  11. Symbiosis Institute of Technology, Pune, India

    Madhura Ingalhalikar

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Yousaf, S., RaviPrakash, H., Anwar, S.M., Sohail, N., Bagci, U. (2020). State-of-the-Art in Brain Tumor Segmentation and Current Challenges. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-66843-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66842-6

  • Online ISBN: 978-3-030-66843-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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