Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN.

Journal: Computational and mathematical methods in medicine
Published Date:

Abstract

BACKGROUND: Pressure injuries (PIs) impose a substantial burden on patients, caregivers, and healthcare systems, affecting an estimated 3 million Americans and costing nearly $18 billion annually. Accurate pressure injury staging remains clinically challenging. Over the last decade, object detection and semantic segmentation have evolved quickly with new methods invented and new application areas emerging. Simultaneous object detection and segmentation paved the way to segment and classify anatomical structures. In this study, we utilize the Mask-R-CNN algorithm for segmentation and classification of stage 1-4 pressure injuries.

Authors

  • Mark Swerdlow
    Department of Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA.
  • Ozgur Guler
    eKare, Inc., Fairfax, VA, USA.
  • Raphael Yaakov
    eKare, Inc., Fairfax, VA, USA.
  • David G Armstrong
    Department of Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA.