Automatic measurement of Cardiothoracic Ratio in chest X-ray images with ProGAN-Generated dataset
|
Full Text |
Pdf
|
|
Author |
G. Jagadeeshawar Reddy, Subba Reddy Borra, A. V. Subba Rao and Vaddithandra Vijaya
|
|
e-ISSN |
1819-6608 |
|
On Pages
|
2080-2087
|
|
Volume No. |
18
|
|
Issue No. |
18
|
|
Issue Date |
November 30, 2023
|
|
DOI |
https://doi.org/10.59018/0923255
|
|
Keywords |
the cardiothoracic ratio (CTR), segmentation, progressive growing of GANs (PGAN), cardiomegaly, CTR computation, classification.
|
Abstract
Cardiomegaly could be identified using the CTR (Cardiothoracic Ratio), which could be assessed on a chest
image or X-ray. It is determined using the link between the size of the heart and chest’s transverse dimension. When the
ratio exceeds a certain threshold, cardiomegaly is diagnosed. The objective of the study is to offer an approach for
calculating the ratio for categorizing cardiomegaly within chest X-ray pictures. The suggested method begins by building
heart and lung segmentation models on the basis of U-Net design utilizing publicly accessible datasets containing lung and
heart mask ground truth. The segmented lung and heart portion sizes are then used to calculate the ratio. Additionally,
chest X-ray images from 3 classes-cardiomegaly, female,and male normal - are created using a novel dataset using PGAN
(Progressive Growing of GANs). This dataset is then utilized to evaluate the suggested solution. The suggested approach is
also utilized to assess the quality of PGAN-generated chest X-ray pictures. In the trials, lung and heart areas in chest
images of X-rays are segmented using trained models and a self-gathered dataset. The computed values of CTR are
contrasted with those that were manually assessed by specialists. The average inaccuracy is 3.08 percent. The models are
then used to segment areas of the lung and heart for CTR computation on the PGAN dataset. The cardiomegaly is then
measured utilizing multiple attempts with varying cut-off threshold values. The proposed approach yields 94.20 percent
specificity,88.31% sensitivity, and 94.61% accuracy with the usual cut-off of 0.50. The suggested approach is shown to be
resilient across hitherto unexplored datasets for computation of CTR, segmentation, as well as cardiomegaly classification,
such as the PGAN dataset. To increase sensitivity, modify the cut-off value to be less than 0.50. The proposed solution is
then assessed from a variety of angles, such as lung and heart segmentation, CTR computation, as well as cardiomegaly
classification. Tests are also carried out on publicly available datasets, self-collected datasets, as well as ProGAN-
reconstructed datasets.
Back