Preliminary Clinical Study of the Differences Between Interobserver Evaluation and Deep Convolutional Neural Network-Based Segmentation of Multiple Organs at Risk in CT Images of Lung Cancer

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

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COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

Frontiers in Medicine ◽

10.3389/fmed.2020.608525 ◽

2020 ◽

Vol 7 ◽

Author(s):

Hayden Gunraj ◽

Linda Wang ◽

Alexander Wong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Ct Images ◽

Ct Imaging ◽

Chest Ct ◽

Deep Convolutional Neural Network ◽

Screening Tools ◽

Screening Methods ◽

Rt Pcr

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3547 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-3

Author(s):

Hayden Gunraj ◽

Linda Wang ◽

Alexander Wong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Screening Method ◽

Ct Images ◽

Chest Ct ◽

Deep Convolutional Neural Network ◽

Screening Tools ◽

Neural Network Architecture ◽

Tremendous Impact

The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images.

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