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• W3156520986 abstract "Abstract Background We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest CT images. Methods We prepared 2358 (347’259, 2D slices) and 180 (17341, 2D slices) volumetric CT images along with their corresponding manual segmentation of lungs and lesions, respectively, in the framework of a multi-center/multi-scanner study. All images were cropped, resized and the intensity values clipped and normalized. A residual network (ResNet) 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 RT-PCR 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. Results 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 – 0.18) and −0.18±3.4% (95% CI, −0.8 - 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, −0.39-2), 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. Conclusion We set out to develop an automated deep learning-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients in order to develop fast, consistent, robust and human error immune framework for lung and pneumonia lesion detection and quantification." @default.
• W3156520986 created "2021-04-26" @default.
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• W3156520986 date "2021-04-13" @default.
• W3156520986 modified "2023-10-14" @default.
• W3156520986 title "COLI-NET: Fully Automated COVID-19 Lung and Infection Pneumonia Lesion Detection and Segmentation from Chest CT Images" @default.
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• W3156520986 doi "https://doi.org/10.1101/2021.04.08.21255163" @default.
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