Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning.
João Otávio Bandeira DinizDarlan Bruno Pontes QuintanilhaAntonino C. dos Santos NetoGiovanni Lucca França da SilvaJonnison Lima FerreiraStelmo Magalhães Barros NettoJosé Denes Lima AraújoLuana Batista da CruzThamila FonteneleCaio M. da S. MartinsMarcos Melo FerreiraVenicius GarciaJosé M. C. BoaroCarolina L. S. CiprianoAristófanes C. SilvaAnselmo Cardoso de PaivaGeraldo Braz JuniorJoão Dallyson Sousa de AlmeidaRodolfo Acatauassu NunesRoberto MogamiMarcelo GattassPublished in: Multim. Tools Appl. (2021)
Keyphrases
- deep learning
- lung parenchyma
- medical images
- ct scans
- ct images
- lymph nodes
- medical imaging
- segmentation algorithm
- unsupervised feature learning
- computed tomography
- image segmentation
- automatic segmentation
- segmentation method
- unsupervised learning
- machine learning
- region growing
- level set
- mental models
- ct data
- lung cancer
- x ray
- multiscale
- carotid artery
- computer tomography
- automatic extraction
- three dimensional
- object segmentation
- learning strategies
- higher order
- weakly supervised
- shape prior
- active contours
- object detection
- pulmonary embolism
- energy function