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

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.

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

“…For segmenting whole lungs, DICOM CT images were segmented utilizing automated deep learning (DL) based segmentation for lungs and COVID-19 pneumonia infectious lesions (COLI-Net) which we have previously developed and extensively evaluated [ 16 ]. In this study we only employed the whole lung segmentation.…”

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

“…The lungs were automatically segmented using our previously developed DL-based algorithm named COLI-Net [ 43 ]. For efficient radiomics feature extraction (feature extraction time), all images were first cropped to the lung region and then resized to 296×216 to obtain a computationally efficient feature extraction.…”

Fighting against COVID‐19: Innovations and applications

“…Moreover, segmentation can enable the development of predictive models that can identify risk factors and predict disease outcomes based on the analysis of segmented image data. 47 Shiri et al 48 present a DL-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest CT images. Zhang et al 49 proposed COVSeg-NET, which is a deep CNN for COVID-19 lung CT image segmentation.…”

“…Shiri et al 48 present a DL‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest CT images. Zhang et al 49 proposed COVSeg‐NET, which is a deep CNN for COVID‐19 lung CT image segmentation.…”