COLI-Net Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

COLI-Net : Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

© 2021 The Authors. International Journal of Imaging Systems and Technology published by Wiley Periodicals LLC.

Bibliographische Detailangaben
Veröffentlicht in:International journal of imaging systems and technology. - 1990. - 32(2022), 1 vom: 08. Jan., Seite 12-25
1. Verfasser: Shiri, Isaac (VerfasserIn)
Weitere Verfasser: 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
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:International journal of imaging systems and technology
Schlagworte:Journal Article COVID‐19 X‐ray CT deep learning pneumonia segmentation
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520 |a 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 along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification 
650 4 |a Journal Article 
650 4 |a COVID‐19 
650 4 |a X‐ray CT 
650 4 |a deep learning 
650 4 |a pneumonia 
650 4 |a segmentation 
700 1 |a Arabi, Hossein  |e verfasserin  |4 aut 
700 1 |a Salimi, Yazdan  |e verfasserin  |4 aut 
700 1 |a Sanaat, Amirhossein  |e verfasserin  |4 aut 
700 1 |a Akhavanallaf, Azadeh  |e verfasserin  |4 aut 
700 1 |a Hajianfar, Ghasem  |e verfasserin  |4 aut 
700 1 |a Askari, Dariush  |e verfasserin  |4 aut 
700 1 |a Moradi, Shakiba  |e verfasserin  |4 aut 
700 1 |a Mansouri, Zahra  |e verfasserin  |4 aut 
700 1 |a Pakbin, Masoumeh  |e verfasserin  |4 aut 
700 1 |a Sandoughdaran, Saleh  |e verfasserin  |4 aut 
700 1 |a Abdollahi, Hamid  |e verfasserin  |4 aut 
700 1 |a Radmard, Amir Reza  |e verfasserin  |4 aut 
700 1 |a Rezaei-Kalantari, Kiara  |e verfasserin  |4 aut 
700 1 |a Ghelich Oghli, Mostafa  |e verfasserin  |4 aut 
700 1 |a Zaidi, Habib  |e verfasserin  |4 aut 
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