Cargando…
COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images
We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams...
Autores principales: | Shiri, Isaac, Arabi, Hossein, Salimi, Yazdan, Sanaat, Amirhossein, Akhavanallaf, Azadeh, Hajianfar, Ghasem, Askari, Dariush, Moradi, Shakiba, Mansouri, Zahra, Pakbin, Masoumeh, Sandoughdaran, Saleh, Abdollahi, Hamid, Radmard, Amir Reza, Rezaei‐Kalantari, Kiara, Ghelich Oghli, Mostafa, Zaidi, Habib |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652855/ https://www.ncbi.nlm.nih.gov/pubmed/34898850 http://dx.doi.org/10.1002/ima.22672 |
Ejemplares similares
-
Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging
por: Salimi, Yazdan, et al.
Publicado: (2021) -
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
por: Shiri, Isaac, et al.
Publicado: (2020) -
High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms
por: Shiri, Isaac, et al.
Publicado: (2022) -
Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains
por: Sanaat, Amirhossein, et al.
Publicado: (2022) -
Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks
por: Salimi, Yazdan, et al.
Publicado: (2023)