COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Suri, Jasjit S.; Agarwal, Sushant; Pathak, Rajesh Kumar; Ketireddy, Vedmanvitha; Columbu, Marta; Saba, Luca; Gupta, Suneet Kumar; Faa, Gavino; Singh, Inder; Turk, Monika; Chadha, Paramjit S.; Johri, Amer M.; Khanna, Narendra N.; Višković, Klaudija; Mavrogeni, Sophie; Laird, John R.; Pareek, Gyan; Miner, Martin; Sobel, David; Balestrieri, Antonella; Sfikakis, Petros P.; Tsoulfas, George; Protogerou, Athanase D.; Misra, Durga Prasanna; Agarwal, Vikas; Kitas, George D.; Teji, Jagjit S.; Al-Maini, Mustafa; Dhanjil, Surinder; Nicolaides, Andrew; Sharma, Aditya; Rathore, V. S.; Fatemi, Mostafa; Alizad, Azra; Krishnan, Prasad; Frence, Nagy; Ruzsa, Zoltán; Gupta, Archna; Naidu, Subbaram; Kalra, Mannudeep K.

doi:10.3390/diagnostics11081405

Cited by 39 publications

(27 citation statements)

References 57 publications

(65 reference statements)

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“…Previously, COVLIAS 1.0 [54] was designed to run on a training: testing ratio of 2:3 dataset from 5000 images. However, this study proposes an inter-observer variability study with K5 in a CV framework.…”

Section: Results and Performance Evaluation 41 Resultsmentioning

confidence: 99%

“…The VGG-Se-gNet architecture used in this study is composed of three parts (i) encoder, (ii) decoder part, and (iii) a pixel-wise SoftMax classifier at the end. It consists of 16 Conv layers compared to the SegNet architecture, where only 13 Conv layers are used [54] in the encoder part. This increase in #layers helps the model extract more features from the image.…”

Section: Three Ai Models: Psp Net Vgg-segnet and Resnet-segnetmentioning

confidence: 99%

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Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

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Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

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Section: Results and Performance Evaluation 41 Resultsmentioning

confidence: 99%

Section: Three Ai Models: Psp Net Vgg-segnet and Resnet-segnetmentioning

confidence: 99%

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

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“…COVID-19 lung segmentation in computed tomography (CT) scans is critical for determining lung severity [ 1 , 2 , 3 ]. According to the World Health Organization (WHO), as of 4 November 2021, more than 247 million individuals have been infected with the acute respiratory syndrome coronavirus 2 (SAR-COV-2).…”

Section: Introductionmentioning

confidence: 99%

“…These methods are time-consuming and subjective for the examination of pulmonary opacities [ 18 , 19 , 20 , 21 ]. As part of the pipeline for COVID-19 diagnosis, CT lung segmentation is crucial [ 1 , 2 , 3 ]. Here is where artificial intelligence (AI) comes into play in automating this time-consuming process and providing a faster diagnosis of the disease [ 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Suri¹,

Agarwal

Carriero

et al. 2021

Diagnostics

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(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

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“…While outstanding progress has been made in ANNs in recent years [4,5] and ANNs are widely used for many practical applications [6][7][8][9][10], conventional predictive ANN models have an obvious limitation since their estimation corresponds to a point estimate. Such a limitation causes the restrictions of using ANN for medical diagnosis, law problems, and portfolio management, where the risk of the predictions is also essential in practice.…”

Section: Introductionmentioning

confidence: 99%

Estimation with Uncertainty via Conditional Generative Adversarial Networks

Lee

Seok

2021

Sensors

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Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample generation. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; moreover, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.

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COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Cited by 39 publications

References 57 publications

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Estimation with Uncertainty via Conditional Generative Adversarial Networks

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