Segmentation of COVID-19 Infections on CT: Comparison of Four UNet-Based Networks

Hasanzadeh, Navid; Sotoudeh-Paima, Saman; Bashirgonbadi, Ali; Naghibi, Mehran; Soltanian‐Zadeh, Hamid

doi:10.1109/icbme51989.2020.9319412

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…A deep learning approach based on the U-Net framework (12) was implemented to segment the COVID-19 infection regions on the CT slices. Previous studies have shown that VGG16-based UNet model was successful in COVID-19 lesion segmentation [11,[15][16][17]. This model can localize abnormal areas in the image and distinguish their boundaries [18].…”

Section: Plos Onementioning

confidence: 95%

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis

Arian,

Mehrabi Nejad,

Zoorpaikar

et al. 2023

PLoS ONE

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Background Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19. Objectives This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists’ performance. Subjects and methods A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups. Results There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes. Conclusions AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.

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Section: Plos Onementioning

confidence: 95%

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis

Arian,

Mehrabi Nejad,

Zoorpaikar

et al. 2023

PLoS ONE

Self Cite

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“…Table 1 presents a summary of the most relevant aspects of each work. All works analyzed, with the exception of Wang et al [Wang et al 2020], Ha-sanzadeh et al [Hasanzadeh et al 2020] and Xu et al [Xu et al 2020], perform a segmentation step of the lung parenchyma. Ouyang et al [Ouyang et al 2020], Müller et al [Müller et al 2020] and Fang et al [Fang et al 2021] use masks of the lung region generated by the architectures proposed by them, as well as in the method proposed by this work.…”

Section: Related Workmentioning

confidence: 99%

Detection of COVID-19 lesions based on computed tomography using U-Net 2.5D and GAN

Landim,

Carvalho,

Diniz

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

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This paper proposes a computational method for automatically detecting suspected regions of COVID-19 from CT scans. COVID-19 has spread rapidly worldwide, infecting over 462 million people and causing over 6 million deaths. There are various methods to diagnose COVID-19, including imaging. The proposed method has five stages, including image acquisition, pre-processing, lung extraction, segmentation of suspected regions using U-Net 2.5D and Pix2Pix architectures, and result validation. The method achieved promising results, with 92% Dice for lung parenchyma segmentation, 82% Dice for suspected region segmentation using U-Net, and 71% Dice using Pix2Pix. It could potentially be integrated into clinical environments as a real aid system.

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“…For example, U‐net networks have been used for the classification and segmentation of COVID‐19 lesions in CT scans. 18 , 19 , 20 The results vary greatly among different studies, partially due to the inter‐ and intra‐observer variations in the training lesion labeling by different radiologists. 18 …”

Section: Introductionmentioning

confidence: 99%

“…For example, U-net networks have been used for the classification and segmentation of COVID-19 lesions in CT scans. [18][19][20] The results vary greatly among different studies, partially due to the inter-and intra-observer variations in the training lesion labeling by different radiologists. 18 Compared to supervised learning, unsupervised learning does not require training labeling, and hence gets rid of the burden of manual lesion delineation and the inter-and intra-observer inconsistency.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing methods are mostly based on supervised learning which requires substantial data labeling by radiologists as training references. For example, U‐net networks have been used for the classification and segmentation of COVID‐19 lesions in CT scans 18–20 . The results vary greatly among different studies, partially due to the inter‐ and intra‐observer variations in the training lesion labeling by different radiologists 18 …”

Section: Introductionmentioning

confidence: 99%

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Label‐free coronavirus disease 2019 lesion segmentation based on synthetic healthy lung image subtraction

Fang

Liu

et al. 2022

Medical Physics

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Purpose Coronavirus disease 2019 (COVID‐19) has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in computed tomography (CT) scans is vital for treatment decision‐making. We proposed a novel unsupervised approach using a cycle consistent generative adversarial network (cycle‐GAN) which automates and accelerates the process of lesion delineation. Method The workflow includes lung volume segmentation, healthy lung image synthesis, infected and healthy image subtraction, and binary lesion mask generation. The lung volume was first delineated using a pre‐trained U‐net and worked as the input for the following network. A cycle‐GAN was trained to generate synthetic healthy lung CT images from infected lung images. After that, the pneumonia lesions were extracted by subtracting the synthetic healthy lung CT images from the infected lung CT images. A median filter and k‐means clustering were then applied to contour the lesions. The auto segmentation approach was validated on three different datasets. Results The average Dice coefficient reached 0.666 ± 0.178 on the three datasets. Especially, the dice reached 0.748 ± 0.121 and 0.730 ± 0.095, respectively, on two public datasets Coronacases and Radiopedia. Meanwhile, the average precision and sensitivity for lesion segmentation on the three datasets were 0.679 ± 0.244 and 0.756 ± 0.162. The performance is comparable to existing supervised segmentation networks and outperforms unsupervised ones. Conclusion The proposed label‐free segmentation method achieved high accuracy and efficiency in automatic COVID‐19 lesion delineation. The segmentation result can serve as a baseline for further manual modification and a quality assurance tool for lesion diagnosis. Furthermore, due to its unsupervised nature, the result is not influenced by physicians’ experience which otherwise is crucial for supervised methods.

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Segmentation of COVID-19 Infections on CT: Comparison of Four UNet-Based Networks

Cited by 9 publications

References 10 publications

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis

Detection of COVID-19 lesions based on computed tomography using U-Net 2.5D and GAN

Label‐free coronavirus disease 2019 lesion segmentation based on synthetic healthy lung image subtraction

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