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...

Full description

Bibliographic Details
Main Author: Silva, Bruno André Pires (author)
Other Authors: Pessanha, Inês (author), Correia-Pinto, Jorge (author), Fonseca, Jaime C. (author), Queirós, Sandro Filipe Monteiro (author)
Format: article
Language:eng
Published: 2022
Subjects:
Pectus excavatum
Computed tomography
Deep learning
Keypoint/landmark detection
Sternum
Indexes
Heating systems
Depression
Biomedical measurement
Correlation
keypoint
landmark detection
Science & Technology
Online Access:http://hdl.handle.net/1822/76564
Country:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/76564

Internet

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

Similar Items