2021
DOI: 10.48550/arxiv.2110.12509
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Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks

Abstract: Estimating the lung depth on x-ray images could provide both an accurate opportunistic lung volume estimation during clinical routine and improve image contrast in modern structural chest imaging techniques like x-ray dark-field imaging. We present a method based on a convolutional neural network that allows a per-pixel lung thickness estimation and subsequent total lung capacity estimation. The network was trained and validated using 5250 simulated radiographs generated from 525 real CT scans. Furthermore, we… Show more

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