Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Journal: Sensors (Basel, Switzerland)
Published Date: Jun 30, 2021

Abstract

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep learning (DL) framework for 2D fetal MRI segmentation using a Cross Attention Squeeze Excitation Network (CASE-Net) for research and clinical applications. CASE-Net is an end-to-end segmentation architecture with relevant modules that are evidence based. The goal of CASE-Net is to emphasize localization of contextual information that is relevant in biomedical segmentation, by combining attention mechanisms with squeeze-and-excitation (SE) blocks. This is a retrospective study with 34 patients. Our experiments have shown that our proposed CASE-Net achieved the highest segmentation Dice score of 87.36%, outperforming other competitive segmentation architectures.

Authors

  • Justin Lo
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.
  • Saiee Nithiyanantham
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.
  • Jillian Cardinell
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.
  • Dylan Young
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.
  • Sherwin Cho
    Institute for Biomedical Engineering, Science and Technology (iBEST), Ryerson University, St. Michael's Hospital, Toronto, ON M5B 2K3, Canada.
  • Abirami Kirubarajan
    Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.
  • Matthias W Wagner
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
  • Roxana Azma
    Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
  • Steven Miller
    Division of Neurology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
  • Mike Seed
    Division of Cardiology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
  • Birgit Ertl-Wagner
    Department of Radiology, Ludwig-Maximilians-University Munich, Munich, Germany.
  • Dafna Sussman
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.

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