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  • 2019-01-10

Automated chest cavity and lung segmentation for temporal tracking of lung pathologies on chest x-ray

Objectively evaluating progression and regression of lung pathologies on frontal Chest X-Ray (CXR) is a challenge. Universally, the temporal evolution of most pathological conditions on chest x-rayis still subjective, whichmight beacceptable for directional prediction butnotreliable for quantification of the change. Here we present a novel Deep Learning (DL) based approach to automatically segment the chest cavity and lungs on a CXR for this purpose. This method will be useful in the evaluation of longitudinal change in relative proportions between the aerated lung and pathological conditions.

Link to complete publication here: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=dqpMNRUAAAAJ&cstart=20&pagesize=80&citation_for_view=dqpMNRUAAAAJ:ufrVoPGSRksC

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