Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
نویسندگان
چکیده
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease even predict outcome. The availability of longitudinal CT series may also result efficient effective method reliably assess progression COVID-19, monitor healing process response different therapeutic strategies. In this paper, we propose a new framework identify infection at voxel level (identification healthy lung, consolidation, opacity) visualize using sequential low-dose non-contrast scans. particular, devise network that utilizes reference scan information improve performance identification. Experimental results on clinical dataset collected our institution show effectiveness proposed compared static deep neural networks for quantification.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2021
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-030-87234-2_26