Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network

OBJECTIVE: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. MATERIALS AND METHODS: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31–8...

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Detalles Bibliográficos
Autores principales: Yoo, Seung-Jin, Yoon, Soon Ho, Lee, Jong Hyuk, Kim, Ki Hwan, Choi, Hyoung In, Park, Sang Joon, Goo, Jin Mo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2021
Materias:
Thoracic Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909864/
https://www.ncbi.nlm.nih.gov/pubmed/33169549
http://dx.doi.org/10.3348/kjr.2020.0318

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909864/
https://www.ncbi.nlm.nih.gov/pubmed/33169549
http://dx.doi.org/10.3348/kjr.2020.0318

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