Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
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Abstract
CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung. The performance of the system was evaluated…
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9Topics & keywords
Topics
Keywords
- Coronavirus disease 2019 (COVID-19)
- Segmentation
- Sørensen–Dice coefficient
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
- Lung
- Radiology
- Medicine
- Artificial intelligence
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