Automatic assessment of Pectus Excavatum severity from CT images using deep learning

Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, which is tedious and prone to variability. In this...

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Detalhes bibliográficos
Autor principal: Silva, Bruno André Pires (author)
Outros Autores: Pessanha, Inês (author), Correia-Pinto, Jorge (author), Fonseca, Jaime C. (author), Queirós, Sandro Filipe Monteiro (author)
Formato: article
Idioma:eng
Publicado em: 2022
Assuntos:
Pectus excavatum
Computed tomography
Deep learning
Keypoint/landmark detection
Sternum
Indexes
Heating systems
Depression
Biomedical measurement
Correlation
keypoint
landmark detection
Science & Technology
Texto completo:http://hdl.handle.net/1822/76564
País:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/76564

Texto Completo

http://hdl.handle.net/1822/76564

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