Full Lung Mask Segmentation in Chest X-rays Using an Ensemble Trained on Digitally Reconstructed Radiographs

Date of Award

5-5-2024

Degree Name

M.S. in Computer Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Russell Hardie

Abstract

This study aims to incorporate some advantages of computed tomographic data into the chest X-ray deep lung segmentation paradigm. We do this by training a deep convolutional neural network on chest radiographs (a.k.a. X-rays) with manually drawn ground truth and an identical network on radiographs digitally reconstructed from computed tomographic data with ground truth generated for the given computed tomographic image using an automated morphological 3D lung segmentation algorithm. The resulting twin-network ensemble generates pairs of lung image segmentation labels for chest X-rays: 1) a “traditional” segmentation of the lungs encompassing the apparently low-density tissue and 2) a novel, “full” lung segmentation encompassing an expanded view of the lungs’ position in a chest X-ray including those regions obscured by the heart, ribs, and viscera, in essence, a 2D projection of any portion of the 3D lung. These networks perform consistently, with mean Intersection-Over-Union scores of > 90% and > 95%, respectively, across five trials. By subjective analysis, the proposed lung segmentation approach shows satisfactory ability to generalize onto genuine check X-ray images. The proposed technique’s high performance and robustness establish a precedent for applying computed tomographic data to automatic chest X-ray segmentation and present an opportunity to further refine existing computer-aided detection and diagnostic tools by considering the full lung.

Keywords

chest X-ray, artificial intelligence, computer aided-detection, computed tomography, digitally reconstructed radiograph, automatic image segmentation

Rights Statement

Copyright 2024, author

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