A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utilized in the well-known BraTS benchmark dataset: T1, T2, Fluid-Attenuated Inversion Recovery (FLAIR), and T1 Contrast-Enhanced (T1CE) are not regularly acquired in clinical practice due to the high cost and long acquisition time. Rather, it is common to utilize limited image modalities for brain tumor segmentation.

Authors

  • Yoonseok Choi
  • Mohammed A Al-Masni
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Kyu-Jin Jung
    Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
  • Roh-Eul Yoo
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Seong-Yeong Lee
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 03080, Republic of Korea.
  • Dong-Hyun Kim
    Neurobiota Research Center, College of Pharmacy, Kyung Hee University, Dongdaemun-gu, Seoul 02447, Republic of Korea.