Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 18, 2023

Abstract

PURPOSE: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction.

Authors

  • Himanshu Rikhari
    Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
  • Esha Baidya Kayal
    Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
  • Shuvadeep Ganguly
    All India Institute of Medical Sciences New Delhi, Medical Oncology, Dr. B.R.A. IRCH, New Delhi, India.
  • Archana Sasi
    All India Institute of Medical Sciences New Delhi, Medical Oncology, Dr. B.R.A. IRCH, New Delhi, India.
  • Swetambri Sharma
    All India Institute of Medical Sciences New Delhi, Medical Oncology, Dr. B.R.A. IRCH, New Delhi, India.
  • D S Dheeksha
    Radiodiagnosis, All India Institute of Medical Sciences New Delhi, New Delhi, India.
  • Manish Saini
    Radiodiagnosis, All India Institute of Medical Sciences New Delhi, New Delhi, India.
  • Krithika Rangarajan
    Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India.
  • Sameer Bakhshi
    Department of Medical Oncology, Dr B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India.
  • Devasenathipathy Kandasamy
    Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
  • Amit Mehndiratta

Keywords

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