ABSTRACT
CT scanning of the chest is one the most important imaging modalities available for pulmonary disease diagnosis. Lung segmentation plays a crucial step in the pipeline of computer-aided analysis and diagnosis. As deep learning models have achieved human-level accuracy in semantic segmentation of anatomical structures, we propose to use trained deep learning models to predict both healthy and infectious areas in chest CT slices. The semantic segmentation results are summarized and visualized using volume rendering technology in the form of roadmaps. The roadmaps consist of both location and volume information that can be used as a location guidance for inspecting suspected pulmonary lesions of chest CT and can possibly be combined into a rapid triage algorithm for treating acute pulmonary diseases.Clinical Relevance- This research applied trained semantic segmentation models in identifying normal lung and pneumonic infection areas to generate a roadmap for assisting medical doctors in browsing chest CT and prognostication.
