Deep learning for semi-automated unidirectional measurement of lung tumor size in CT.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Jun 23, 2021

Abstract

BACKGROUND: Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement.

Authors

  • MinJae Woo
    Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA.
  • A Michael Devane
    Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA.
  • Steven C Lowe
    Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA.
  • Ervin L Lowther
    Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA.
  • Ronald W Gimbel
    Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA. rgimbel@clemson.edu.

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