Machine learning and deep learning for brain tumor MRI image segmentation.

Journal: Experimental biology and medicine (Maywood, N.J.)
Published Date: Dec 16, 2023

Abstract

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Authors

  • Md Kamrul Hasan Khan
    National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.
  • Wenjing Guo
    National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Fan Dong
    National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.
  • Zoe Li
    Department of Civil Engineering, McMaster University, Hamilton, ON, L8S 4L8, Canada.
  • Tucker A Patterson
    National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States.
  • Huixiao Hong
    National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Electronic address: Huixiao.Hong@fda.hhs.gov.

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