Chest X-ray Dataset with Lung Segmentation

Chest X-ray(CXR) images are prominent among medical images and are commonly
administered in emergency diagnosis and treatment corresponding to cardiac and
respiratory diseases. Though there are robust solutions available for medical
diagnosis, validation of artificial intelligence (AI) in radiology is still
questionable. Segmentation is pivotal in chest radiographs that aid in
improvising the existing AI-based medical diagnosis process. We provide the
CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large
dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a
popular CXR image dataset. The dataset contains segmentation results of
243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks.
Additionally, this dataset can be utilized for computer vision-related deep
learning tasks such as medical image classification, semantic segmentation and
medical report generation. Models using segmented images yield better results
since only the features related to the important areas of the image are
focused. Thus images of this dataset can be manipulated to any visual feature
extraction process associated with the original MIMIC-CXR dataset and enhance
the results of the published or novel investigations. Furthermore, masks
provided by this dataset can be used to train segmentation models when
combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80%
in dice similarity coefficient and 91.97% in IoU for lung segmentation using
CXLSeg.