Combination of 2D and 3D nnU-Net for ground glass opacity segmentation in CT images of Post-COVID-19 patients.

Journal: Computers in biology and medicine
Published Date: Jun 20, 2025

Abstract

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic plays a significant roles in the global health, highlighting the imperative for effective management of post-recovery symptoms. Within this context, Ground Glass Opacity (GGO) in lung computed tomography CT scans emerges as a critical indicator for early intervention. Recently, most researchers have investigated initially a challenge to refine techniques for GGO segmentation. These approaches aim to scrutinize and juxtapose cutting-edge methods for analyzing lung CT images of patients recuperating from COVID-19. While many methods in this challenge utilize the nnU-Net architecture, its general approach has not concerned completely GGO areas such as marking infected areas, ground-glass opacity, irregular shapes and fuzzy boundaries. This research has investigated a specialized machine learning algorithm, advancing the nn-UNet framework to accurately segment GGO in lung CT scans of post-COVID-19 patients.

Authors

  • Quang H Nguyen
    School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi 100000, Vietnam.
  • Duc A Hoang
    School of Information and Communications Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hai Ba Trung, Hanoi, Viet Nam.
  • Hai Van Pham
    School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Le Dai Hanh, Hai Ba Trung, Hanoi City 10000, Vietnam.

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