Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays
using Convolutional Neural Networks
- URL: http://arxiv.org/abs/2110.12509v2
- Date: Wed, 27 Oct 2021 09:02:30 GMT
- Title: Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays
using Convolutional Neural Networks
- Authors: Manuel Schultheiss, Philipp Schmette, Thorsten Sellerer, Rafael
Schick, Kirsten Taphorn, Korbinian Mechlem, Lorenz Birnbacher, Bernhard
Renger, Marcus R. Makowski, Franz Pfeiffer, Daniela Pfeiffer
- Abstract summary: Estimating the lung depth on x-ray images could provide both an accurate opportunistic lung volume estimation during clinical routine.
We present a method based on a convolutional neural network that allows a per-pixel lung thickness estimation and subsequent total lung capacity estimation.
- Score: 4.526265435158766
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 are able to infer
the model trained with simulation data on real radiographs.
For 35 patients, quantitative and qualitative evaluation was performed on
standard clinical radiographs. The ground-truth for each patient's total lung
volume was defined based on the patients' corresponding CT scan. The
mean-absolute error between the estimated lung volume on the 35 real
radiographs and groundtruth volume was 0.73 liter. Additionally, we predicted
the lung thicknesses on a synthetic dataset of 131 radiographs, where the
mean-absolute error was 0.27 liter. The results show, that it is possible to
transfer the knowledge obtained in a simulation model to real x-ray images.
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