Heart and lung segmentations for MIMIC-CXR/MIMIC-CXR-JPG and Montgomery County TB databases

Segmenting the heart and lungs from chest X-ray images is essential for
accurate disease diagnosis, enabling the calculation of image-derived digital
biomarkers for cardiopulmonary health assessment. Furthermore, creation of
annotated data sets can enhance development of machine learning applications
in disease detection, reduce image noise for better clarity, or promote
standardization for tracking disease progression or treatment effects.
Generation of manual segmentations is time intensive process and it also
requires access to trained medical professionals to verify the quality and
accuracy of the scans. Originally made to train heart and lung
segmentation/detection models for cardiomegaly diagnosis, the manual
segmentations in this data paper are published here in hope of aiding
development of AI models relating to heart and lungs identification in chest
X-rays. This database presents the heart and lung segmentations for 200 semi-
randomly chosen MIMIC-CXR/MIMIC-CXR-JPG posterior-anterior chest X-rays for
the purpose of training detection and segmentation networks. Additionally, it
contains the heart segmentations for the 138 posterior-anterior chest X-rays
in the Montgomery Country tuberculosis database.