Chest X-ray segmentation images based on MIMIC-CXR

As more and more artificial intelligence (AI) or deep learning technologies
have been applied to medical image applications such as radiological finding
identification in chest X-rays (CXRs), the interpretability of the prediction
model is crucial for building trust in AI. In pulmonary pathology detection,
the CXR images with proper anatomical segmentations could aid in interpreting
the models. However, the accuracy of the auto-segmentation algorithms was not
high enough to create such a benchmark. In this project, we provided
segmentation results of 1,141 frontal-view CXRs randomly selected from the
MIMIC-CXR database. These CXRs were first processed into a pair of segmented
images with the lung lobes and the rest parts by deep learning-based
algorithms. We then manually filtered out the incorrect segmentation results.
The segmented images maybe helpful for model interpretability.