MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

Journal: Medical physics
Published Date: Aug 23, 2022

Abstract

BACKGROUND: Accurate and automated brain tumor segmentation from multi-modality MR images plays a significant role in tumor treatment. However, the existing approaches mainly focus on the fusion of multi-modality while ignoring the correlation between single-modality and tumor subcomponents. For example, T2-weighted images show good visualization of edema, and T1-contrast images have a good contrast between enhancing tumor core and necrosis. In the actual clinical process, professional physicians also label tumors according to these characteristics. We design a method for brain tumors segmentation that utilizes both multi-modality fusion and single-modality characteristics.

Authors

  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Yuchen Jiang
    Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
  • Minglei Li
    Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
  • Jiusi Zhang
    Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
  • Shen Yin
    Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
  • Hao Luo
    School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.

Keywords

External Resources

Popular Topics
Recent Journals