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 Publisher

Shiri I¹ ; Arabi H¹ ; Salimi Y¹ ; Sanaat A¹ ; Akhavanallaf A¹ ; Hajianfar G² ; Askari D³ ; Moradi S⁴ ; Mansouri Z¹ ; Pakbin M⁵ ; Sandoughdaran S⁶ ; Abdollahi H⁷ ; Radmard AR⁸ ; Rezaeikalantari K² Show All Authors

Shiri I¹
Arabi H¹
Salimi Y¹
Sanaat A¹
Akhavanallaf A¹
Hajianfar G²
Askari D³
Moradi S⁴
Mansouri Z¹
Pakbin M⁵
Sandoughdaran S⁶
Abdollahi H⁷
Radmard AR⁸
Rezaeikalantari K²
Ghelich Oghli M^{4, 9}
Zaidi H^{1, 10, 11, 12}

Show Affiliations

1. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
2. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
3. Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4. Research and Development Department, Med Fanavaran Plus Co., Karaj, Iran
5. Clinical Research Development Center, Qom University of Medical Sciences, Qom, Iran
6. Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
7. Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
8. Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
9. Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
10. Geneva University Neurocenter, Geneva University, Geneva, Switzerland
11. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
12. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

Source: International Journal of Imaging Systems and Technology Published:2022

Abstract

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

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Style	Citing Format
MLA	Shiri I, et al.. "Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images." International Journal of Imaging Systems and Technology, vol. 32, no. 1, 2022, pp. 12-25.
APA	Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, Moradi S, Mansouri Z, Pakbin M, Sandoughdaran S, Abdollahi H, Radmard AR, Rezaeikalantari K, Ghelich Oghli M, Zaidi H (2022). Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images. International Journal of Imaging Systems and Technology, 32(1), 12-25.
Chicago	Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, et al.. "Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images." International Journal of Imaging Systems and Technology 32, no. 1 (2022): 12-25.
Harvard	Shiri I et al. (2022) 'Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images', International Journal of Imaging Systems and Technology, 32(1), pp. 12-25.
Vancouver	Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, et al.. Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images. International Journal of Imaging Systems and Technology. 2022;32(1):12-25.
BibTex	@article{ author = {Shiri I and Arabi H and Salimi Y and Sanaat A and Akhavanallaf A and Hajianfar G and Askari D and Moradi S and Mansouri Z and Pakbin M and Sandoughdaran S and Abdollahi H and Radmard AR and Rezaeikalantari K and Ghelich Oghli M and Zaidi H}, title = {Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images}, journal = {International Journal of Imaging Systems and Technology}, volume = {32}, number = {1}, pages = {12-25}, year = {2022} }
RIS	TY - JOUR AU - Shiri I AU - Arabi H AU - Salimi Y AU - Sanaat A AU - Akhavanallaf A AU - Hajianfar G AU - Askari D AU - Moradi S AU - Mansouri Z AU - Pakbin M AU - Sandoughdaran S AU - Abdollahi H AU - Radmard AR AU - Rezaeikalantari K AU - Ghelich Oghli M AU - Zaidi H TI - Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images JO - International Journal of Imaging Systems and Technology VL - 32 IS - 1 SP - 12 EP - 25 PY - 2022 ER -

Style

Citing Format

MLA

Shiri I, et al.. "Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images." International Journal of Imaging Systems and Technology, vol. 32, no. 1, 2022, pp. 12-25.

APA

Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, Moradi S, Mansouri Z, Pakbin M, Sandoughdaran S, Abdollahi H, Radmard AR, Rezaeikalantari K, Ghelich Oghli M, Zaidi H (2022). Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images. International Journal of Imaging Systems and Technology, 32(1), 12-25.

Chicago

Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, et al.. "Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images." International Journal of Imaging Systems and Technology 32, no. 1 (2022): 12-25.

Harvard

Shiri I et al. (2022) 'Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images', International Journal of Imaging Systems and Technology, 32(1), pp. 12-25.

Vancouver

Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, et al.. Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images. International Journal of Imaging Systems and Technology. 2022;32(1):12-25.

BibTex

@article{ author = {Shiri I and Arabi H and Salimi Y and Sanaat A and Akhavanallaf A and Hajianfar G and Askari D and Moradi S and Mansouri Z and Pakbin M and Sandoughdaran S and Abdollahi H and Radmard AR and Rezaeikalantari K and Ghelich Oghli M and Zaidi H}, title = {Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images}, journal = {International Journal of Imaging Systems and Technology}, volume = {32}, number = {1}, pages = {12-25}, year = {2022} }

RIS

TY - JOUR
AU - Shiri I
AU - Arabi H
AU - Salimi Y
AU - Sanaat A
AU - Akhavanallaf A
AU - Hajianfar G
AU - Askari D
AU - Moradi S
AU - Mansouri Z
AU - Pakbin M
AU - Sandoughdaran S
AU - Abdollahi H
AU - Radmard AR
AU - Rezaeikalantari K
AU - Ghelich Oghli M
AU - Zaidi H
TI - Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images
JO - International Journal of Imaging Systems and Technology
VL - 32
IS - 1
SP - 12
EP - 25
PY - 2022
ER -

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