Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

Hofmanninger, Johannes; Prayer, Florian; Pan, Jeanny; Röhrich, Sebastian; Prosch, Helmut; Langs, Georg

doi:10.1186/s41747-020-00173-2

Cited by 393 publications

(345 citation statements)

References 38 publications

(87 reference statements)

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“…The dataset source website offers image masks to segment the lung regions. These masks were created automatically based on [ 27 ]. The automated lung segmentation model can be found in the GitHub repository JoHo/lungmask.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

2021

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Background Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, and , are investigated for semantically segmenting infected tissue regions in CT lung images. Methods We propose to use two known deep learning networks, and , for image tissue classification. is characterized as a scene segmentation network and as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. Results The results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor (with 0.91 mean accuracy). Conclusion Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

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Section: Methodsmentioning

confidence: 99%

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

2021

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“…annotated the lung opacities slice by slice in 105 CT volumes from Site A. For lung lobe segmentation, we adopted the automated lung segmentation method proposed by Hofmanninger et al [ 6 ]. Their work provides a trained U-net model for lung segmentation.…”

Section: Ai-assisted Severity Assessmentmentioning

confidence: 99%

Association of AI quantified COVID-19 chest CT and patient outcome

et al. 2021

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Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. Methods We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). Results AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, p < 0.001). Using AI-based scores produced significantly higher ( p < 0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. Conclusions Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

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“…Chest CT scans of patients were collected upon initial hospitalization and preprocessed using intensity normalization, contrast limited adaptive histogram equalization, and gamma adjustment, using the same preprocessing pipeline as in our previous study [ 43 ]. We performed lung segmentation in the chest CT images by using an established model “R231CovidWeb” [ 44 ], which was pretrained using a large, diverse data set of non–COVID-19 chest CT scans and further fine-tuned with an additional COVID-19 data set [ 45 ]. CT slices with <3 mm 2 of lung tissue were excluded from the data sets since they provide limited or no information about the lung.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Quiroz¹,

Feng²,

Cheng³

et al. 2021

JMIR Med Inform

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Background COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. Objective This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. Methods Clinical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. Results Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). Conclusions Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

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Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

Cited by 393 publications

References 38 publications

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Association of AI quantified COVID-19 chest CT and patient outcome

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

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