A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.

Journal: International journal of radiation oncology, biology, physics
Published Date: Jun 4, 2022

Abstract

PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans.

Authors

  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Ying Liang
    Department of Therapeutic Radiology, Yale University, New Haven, CT, U.S.A.
  • Jie Ding
    State Key Laboratory of Respiratory Disease, Joint School of Life Sciences, Guangzhou Chest Hospital, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, China.
  • Asma Amjad
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Eric Paulson
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Ergun Ahunbay
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • William A Hall
    Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin. Electronic address: whall@mcw.edu.
  • Beth Erickson
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • X Allen Li
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

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