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 Originally made to train heart and lung segmentation/detection models for cardiomegaly diagnosis, the manual segmentations in this data paper are published here in hope of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.