UNet Based Pipeline for Lung Segmentation from Chest X-Ray Images
This work addresses the need for automated lung segmentation in medical centers, particularly in resource-constrained locations, but it is incremental as it applies an existing method to a specific dataset.
The paper tackles lung segmentation from chest X-ray images by developing an end-to-end pipeline using UNet trained on the JSRT dataset, resulting in faster processing for screening lung disorders.
Biomedical image segmentation is one of the fastest growing fields which has seen extensive automation through the use of Artificial Intelligence. This has enabled widespread adoption of accurate techniques to expedite the screening and diagnostic processes which would otherwise take several days to finalize. In this paper, we present an end-to-end pipeline to segment lungs from chest X-ray images, training the neural network model on the Japanese Society of Radiological Technology (JSRT) dataset, using UNet to enable faster processing of initial screening for various lung disorders. The pipeline developed can be readily used by medical centers with just the provision of X-Ray images as input. The model will perform the preprocessing, and provide a segmented image as the final output. It is expected that this will drastically reduce the manual effort involved and lead to greater accessibility in resource-constrained locations.