Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
Abstract
:1. Introduction
2. Methodology
2.1. Patient Demographics, Image Acquisition, and Data Preparation
2.1.1. Demographics
2.1.2. Image Acquisition
2.1.3. Data Preparation
2.2. Architecture
2.2.1. Three AI Models: PSP Net, VGG-SegNet, and ResNet-SegNet
2.2.2. Loss Functions for AI Models
3. Experimental Protocol
3.1. Accuracy Estimation of AI Models Using Cross-Validation
3.2. Lung Quantification
3.3. AI Model Accuracy Computation
4. Results and Performance Evaluation
4.1. Results
4.2. Performance Evaluation
4.2.1. Lung Boundary and Long Axis Visualization
4.2.2. Performance Metrics for the Lung Area Error
Cumulative Frequency Plot for Lung Area Error
Correlation Plot for Lung Area Error
Jaccard Index and Dice Similarity
Bland-Altman Plot for Lung Area
ROC Plots for Lung Area
4.2.3. Performance Evaluation Using Lung Long Axis Error
Cumulative Frequency Plot for Lung Long Axis Error
Correlation Plot for Lung Long Axis Error
Bland-Altman Plots for Lung Long Axis Error
Statistical Tests
Figure of Merit
5. Discussion
5.1. A Special Note on Three Model Behaviors with Respect to the Two OBSERVERS
5.2. Benchmarking
5.3. Strengths, Weakness, and Extensions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SN | Symbol | Description of the Symbols |
1 | ACC (ai) | Accuracy |
2 | AE | Area Error |
3 | AI | Artificial Intelligence |
4 | ARDS | Acute Respiratory Distress Syndrome |
5 | AUC | Area Under the Curve |
6 | BA | Bland-Altman |
7 | BE | Boundary Error |
8 | CC | Correlation coefficient |
9 | CE | Cross Entropy |
10 | COVID | Coronavirus disease |
11 | COVLIAS | COVID Lung Image Analysis System |
12 | CT | Computed Tomography |
13 | DL | Deep Learning |
14 | DS | Dice Similarity |
15 | FoM | Figure of merit |
16 | GT | Ground Truth |
17 | HDL | Hybrid Deep Learning |
18 | IS | Image Size |
19 | JI | Jaccard Index |
20 | LAE | Lung Area Error |
21 | LLAE | Lung Long Axis Error |
22 | NIH | National Institute of Health |
23 | PC | Pixel Counting |
24 | RF | Resolution Factor |
25 | ROC | Receiver operating characteristic |
26 | SDL | Solo Deep Learning |
27 | VGG | Visual Geometric Group |
28 | VS | Variability studies |
29 | WHO | World Health Organization |
Symbols
SN | Symbol | Description of the Symbols |
1 | Cross Entropy-loss | |
2 | m | Model used for segmentation in the total number of models M |
3 | n | Image scan number in total number N |
4 | Mean estimated lung area for all images using AI model ‘m’ | |
5 | Estimated Lung Area using AI model ‘m’ and image ‘n’ | |
6 | GT lung area for image ‘n’ | |
7 | Mean ground truth area for all images N in the database | |
8 | Mean estimated lung long axis for all images using AI model ‘m’ | |
9 | Estimated lung long axis using AI model ‘m’ and image ‘n’ | |
10 | GT lung long axis for image ‘n’ | |
11 | Mean ground truth long axis for all images N in the database | |
12 | Figure-of-Merit for segmentation model ‘m’ | |
14 | Figure-of-Merit for long axis for model ‘m’ | |
15 | JI | Jaccard Index for a specific segmentation model |
16 | DSC | Dice Similarity Coefficient for a specific segmentation model |
17 | TP, TN | True Positive and True Negative |
18 | FP, FN | False Positive and False Negative |
19 | xi | GT label |
20 | pi | SoftMax classifier probability |
21 | Yp | Ground truth image |
22 | Estimated image | |
23 | P | Total no of pixels in an image in x, y-direction |
24 | K5 | Cross-validation protocol with 80% training and 20% testing (5 folds) |
Deep Learning Segmentation Architectures | ||
25 | PSP Net | SDL model for lung segmentation with pyramidal feature extraction |
26 | VGG-SegNet | HDL model designed by fusion of VGG-19 and SegNet architecture |
27 | ResNet-SegNet | HDL model designed by fusion of ResNet-50 and SegNet architecture |
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PSP Net | VGG-SegNet | ResNet-SegNet | |||||||
---|---|---|---|---|---|---|---|---|---|
Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | |
Observer 1 | 0.98 | 0.98 | 0.98 | 0.98 | 0.99 | 0.99 | 0.98 | 0.98 | 0.98 |
Observer 2 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 |
% Difference | 0.00 | 0.00 | 0.00 | 0.00 | 1.01 | 0.51 | 2.04 | 2.04 | 2.04 |
PSP Net | VGG-SegNet | ResNet-SegNet | |||||||
---|---|---|---|---|---|---|---|---|---|
Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | |
Observer 1 | 0.97 | 0.99 | 0.98 | 0.96 | 0.97 | 0.97 | 0.98 | 0.99 | 0.99 |
Observer 2 | 0.96 | 0.98 | 0.97 | 0.96 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 |
% Difference | 1.03 | 1.01 | 1.02 | 0.00 | 0.00 | 0.00 | 0.00 | 1.01 | 0.51 |
Lung Area | Lung Long Axis | ||||||||
---|---|---|---|---|---|---|---|---|---|
SN | Combinations | Paired t-Test (p-Value) | Wilcoxon (p-Value) | ANOVA (p-Value) | CC [0–1] | Paired t-Test (p-Value) | Wilcoxon (p-Value) | ANOVA (p-Value) | CC [0–1] |
1 | P1 vs. V1 | <0.0001 | <0.0001 | <0.001 | 0.9726 | <0.0001 | <0.0001 | <0.001 | 0.9509 |
2 | P1 vs. R1 | <0.0001 | <0.0001 | <0.001 | 0.9514 | <0.0001 | <0.0001 | <0.001 | 0.9506 |
3 | P1 vs. P2 | <0.0001 | <0.0001 | <0.001 | 0.9703 | <0.0001 | <0.0001 | <0.001 | 0.9686 |
4 | P1 vs. V2 | <0.0001 | <0.0001 | <0.001 | 0.9446 | <0.0001 | <0.0001 | <0.001 | 0.9445 |
5 | P1 vs. R2 | <0.0001 | <0.0001 | <0.001 | 0.9764 | <0.0001 | <0.0001 | <0.001 | 0.9661 |
6 | V1 vs. R1 | <0.0001 | <0.0001 | <0.001 | 0.9663 | <0.0001 | <0.0001 | <0.001 | 0.9561 |
7 | V1 vs. P2 | <0.0001 | <0.0001 | <0.001 | 0.9726 | <0.0001 | <0.0001 | <0.001 | 0.9671 |
8 | V1 vs. V2 | <0.0001 | <0.0001 | <0.001 | 0.9766 | <0.0001 | <0.0001 | <0.001 | 0.9638 |
9 | V1 vs. R2 | <0.0001 | <0.0001 | <0.001 | 0.9943 | <0.0001 | <0.0001 | <0.001 | 0.9796 |
10 | R1 vs. P2 | <0.0001 | <0.0001 | <0.001 | 0.9549 | <0.0001 | <0.0001 | <0.001 | 0.9617 |
11 | R1 vs. V2 | <0.0001 | <0.0001 | <0.001 | 0.9513 | <0.0001 | <0.0001 | <0.001 | 0.9499 |
12 | R1 vs. R2 | <0.0001 | <0.0001 | <0.001 | 0.9690 | <0.0001 | <0.0001 | <0.001 | 0.9726 |
Observer 1 | Observer 2 | % Difference | Hypothesis (<5%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | |
PSP Net | 95.07 | 95.11 | 95.09 | 97.37 | 97.49 | 97.43 | 2% | 3% | 2% | ✓ | ✓ | ✓ |
VGG-SegNet | 96.73 | 97.40 | 97.04 | 97.74 | 97.27 | 97.52 | 1% | 0% | 0% | ✓ | ✓ | ✓ |
ResNet-SegNet | 98.33 | 99.98 | 99.11 | 97.88 | 99.20 | 98.50 | 0% | 1% | 1% | ✓ | ✓ | ✓ |
Observer 1 | Observer 2 | % Difference | Hypothesis (<5%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | Left | Right | Mean | |
PSP Net | 98.91 | 97.34 | 98.13 | 98.65 | 98.60 | 98.62 | 0% | 1% | 1% | ✓ | ✓ | ✓ |
VGG-SegNet | 99.41 | 98.50 | 98.95 | 97.07 | 97.27 | 97.17 | 2% | 1% | 2% | ✓ | ✓ | ✓ |
ResNet-SegNet | 99.73 | 99.37 | 99.83 | 99.51 | 98.75 | 99.13 | 0% | 1% | 1% | ✓ | ✓ | ✓ |
Observer 1 | Observer 2 | Mean Obs. 1 & Obs. 2 | |||||||
---|---|---|---|---|---|---|---|---|---|
Attributes | PSP Net | VGG-SegNet | ResNet-SegNet | PSP Net | VGG-SegNet | ResNet-SegNet | PSP Net | VGG-SegNet | ResNet-SegNet |
DS | 0.96 | 0.98 | 0.98 | 0.96 | 0.95 | 0.97 | 0.96 | 0.97 | 0.98 |
JI | 0.93 | 0.96 | 0.97 | 0.92 | 0.9 | 0.94 | 0.93 | 0.93 | 0.96 |
CC Left LA | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 1 | 0.98 | 0.98 | 0.99 |
CC Right LA | 0.98 | 0.99 | 0.98 | 0.98 | 0.98 | 1 | 0.98 | 0.99 | 0.99 |
CC Left LLA | 0.97 | 0.96 | 0.98 | 0.96 | 0.96 | 0.98 | 0.97 | 0.96 | 0.98 |
CC Right LLA | 0.99 | 0.97 | 0.99 | 0.98 | 0.97 | 0.98 | 0.99 | 0.97 | 0.99 |
CF Left LA < 10% | 0.83 | 0.85 | 0.90 | 0.81 | 0.75 | 0.89 | 0.82 | 0.80 | 0.89 |
CF Right LA < 10% | 0.78 | 0.85 | 0.90 | 0.80 | 0.75 | 0.88 | 0.79 | 0.80 | 0.89 |
Aggregate Score | 7.42 | 7.54 | 7.67 | 7.39 | 7.24 | 7.64 | 7.40 | 7.39 | 7.66 |
Attributes/Author | Saba et al. [49] | Jeremy et al. [77] | Joskowicz et al. [78] | Suri et al. (Proposed) |
---|---|---|---|---|
# of patients | 96 | 33 | 18 | 72 |
# of Images | NA | NA | 490 | 5000 |
# of Observers | 3 | 5 | 11 | 2 |
Dataset | Non-COVID | Non-COVID | Non-COVID | COVID |
Image Size | 512 | NA | 512 | 768 |
# of tests/PE | 5 | 0 | 2 | 13 |
CC | 0.98 | NA | NA | 0.98 |
Boundary estimation | Manual | Manual | Manual | Manual & automatic |
AI Models | NA | NA | NA | 3 |
Modality | CT | CT | CT | CT |
Area Error | ✓ | ✓ | ✗ | ✓ |
Boundary Error | ✓ | ✗ | ✗ | ✓ |
ROC | ✗ | ✗ | ✗ | ✓ |
JI | ✓ | ✗ | ✗ | ✓ |
DS | ✓ | ✗ | ✗ | ✓ |
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Suri, J.S.; Agarwal, S.; Elavarthi, P.; Pathak, R.; Ketireddy, V.; Columbu, M.; Saba, L.; Gupta, S.K.; Faa, G.; Singh, I.M.; et al. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics 2021, 11, 2025. https://doi.org/10.3390/diagnostics11112025
Suri JS, Agarwal S, Elavarthi P, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, et al. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics. 2021; 11(11):2025. https://doi.org/10.3390/diagnostics11112025
Chicago/Turabian StyleSuri, Jasjit S., Sushant Agarwal, Pranav Elavarthi, Rajesh Pathak, Vedmanvitha Ketireddy, Marta Columbu, Luca Saba, Suneet K. Gupta, Gavino Faa, Inder M. Singh, and et al. 2021. "Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography" Diagnostics 11, no. 11: 2025. https://doi.org/10.3390/diagnostics11112025