Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment
Abstract
:1. Introduction
- The publication and use of COVID-19_CHDSET Dataset, a new CT dataset of COVID-19 infected patients from Milan, a region early and intensively involved in the pandemic. The CTs are labeled according the patient hospitalization outcome.
- The introduction of a novel, staged, data-driven technique of spatially coherent sub-sampling of CT volumes by detecting Volumes-of-Interest (VoI). This leads to lightweight but highly informative CT data that allow us to train state-of-the-art 2D and 3D classifiers more effectively than existing techniques.
- The exhaustive experimentation among various CT-based risk-prediction approaches and 2D/3D classifiers for COVID-19 patient stratification, to assess their performance.
2. Related Work
3. COVID-19_CHDSET Dataset
4. COVID-19 CT-Based Patient Risk Assessment
4.1. COVID-19 Quantification for VoI Detection
4.2. VoI Classification for COVID-19 Patient Risk Assessment
5. Evaluation
5.1. Experimental Setup
5.1.1. COVID-19_CHDSET Dataset Splits
5.1.2. CT-Based Risk Assessment Strategies
- SSoI: is applied in a similar fashion to VoI. Precisely, to avoid any lossy volume down-sampling, by essentially reducing the height of the treated volume, we experimented with an alternative approach based on recent works [16,39]. In detail, CT slices are analyzed to quantify the infection, and the most infected ones (not necessarily consecutive) are considered to compose a stack of slices-of-interest (SSoI); Volume: the second and mostly common approach we experimented with, considers the whole volume and down-scales it to reach manageable data sizes and computational time.
5.1.3. Evaluation Metrics
5.1.4. Radiologists’ CT-Based Risk Assessment Protocol
5.2. Results
5.2.1. Lesion Segmentation Results
5.2.2. CT-Based Risk Assessment Results
6. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Implementation Details
References
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COVID-19_CHDSET | Training Set | Testing Set | Total |
---|---|---|---|
moderate risk | 94 | 24 | 118 |
severe risk | 374 | 94 | 468 |
extreme risk | 31 | 9 | 40 |
Total | 499 | 127 | 626 |
COVID-19_CHDSETOS | Training Set | Testing Set | Total |
moderate risk | 332 | 24 | 356 |
severe risk | 374 | 94 | 468 |
extreme risk | 341 | 9 | 350 |
COVID-19_CHDSETUS | Training Set | Testing Set | Total |
moderate risk | 31 | 9 | 40 |
severe risk | 31 | 9 | 40 |
extreme risk | 31 | 9 | 40 |
mDice (%) | mIoU (%) | |
---|---|---|
Lesion Seg. | 97.8 (+1.1–7.9%) | 95.6 (+1.5–8.7%) |
Acc (%) ↑ | Sens (%) ↑ | Spec (%) ↑ | F1 (%) ↑ | ||
---|---|---|---|---|---|
3D | ResNet2Plus1D-VoI | 79.58 | 70.29 | 83.67 | 70.02 |
ResNet2Plus1D-SSoI | 74.01 | 33.33 | 66.67 | 28.35 | |
2D | DenseNet201-VoI | 81.42 | 84.45 | 87.32 | 78.82 |
DenseNet201-SSoI | 82.85 | 81.37 | 87.54 | 79.15 |
Model | Acc (%) ↑ | Sens (%) ↑ | Spec (%) ↑ | F1 (%) ↑ | |
---|---|---|---|---|---|
3D | ResNet2Plus1D-VoI | 62.90 | 62.90 | 81.4 | 63.06 |
ResNet2Plus1D-SSoI | 33.33 | 33.33 | 66.66 | 16.66 | |
2D | DenseNet201-VoI | 88.88 | 89.77 | 94.73 | 88.88 |
DenseNet201-SSoI | 82.33 | 82.57 | 90.89 | 82.33 | |
Radiologists | 40.74 | 39.19 | 70.37 | 39.05 |
Model | Acc (%) ↑ | Sens (%) ↑ | Spec (%) ↑ | F1 (%) ↑ | |
---|---|---|---|---|---|
3D | ResNet3D-VoI | 66.67 | 66.67 | 83.31 | 65.85 |
ResNet3D-RVoI | 72.54 | 51.97 | 77.58 | 52.32 | |
MixedConv-VoI | 51.85 | 51.85 | 75.92 | 45.71 | |
MixedConv-RVoI | 66.2 | 46.35 | 73.10 | 45.78 | |
ResNet2Plus1D-VoI | 62.90 | 62.90 | 81.4 | 63.06 | |
ResNet2Plus1D-RVoI | 42.85 | 33.33 | 66.66 | 20.00 | |
ResNet2Plus1D-Volume | 33.33 | 33.33 | 66.66 | 16.66 | |
2D | ResNet101-RVoI | 76.42 | 83.80 | 86.35 | 76.42 |
ResNet101-VoI | 85.18 | 85.60 | 92.32 | 85.60 | |
DenseNet201-RVoI | 77.77 | 80.68 | 89.47 | 78.88 | |
DenseNet201-VoI | 88.88 | 89.77 | 94.73 | 88.88 | |
DenseNet201-Volume | 72.14 | 72.68 | 79.58 | 61.33 |
Acc (%) ↑ | Sens (%) ↑ | Spec (%) ↑ | F1 (%) ↑ | ||
---|---|---|---|---|---|
3D | ResNet3D-VoI | 80.99 | 71.14 | 82.93 | 71.11 |
ResNet3D-RVoI | 68.31 | 52.32 | 73.69 | 52.32 | |
MixedConv-VoI | 76.76 | 67.31 | 82.07 | 65.85 | |
MixedConv-RVoI | 67.61 | 57.47 | 75.64 | 55.55 | |
ResNet2Plus1D-VoI | 79.58 | 70.29 | 83.67 | 70.02 | |
ResNet2Plus1D-RVoI | 71.83 | 42.11 | 73.71 | 41.82 | |
ResNet2Plus1D-Volume | 68.84 | 33.33 | 66.67 | 27.18 | |
2D | ResNet101-RVoI | 76.00 | 66.30 | 77.44 | 69.86 |
ResNet101-VoI | 76.42 | 83.80 | 86.35 | 77.27 | |
DenseNet201-RVoI | 74.29 | 74.27 | 81.80 | 74.29 | |
DenseNet201-VoI | 81.42 | 84.45 | 87.32 | 78.82 | |
DenseNet201-Volume | 68.10 | 53.07 | 69.98 | 46.45 |
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Chatzitofis, A.; Cancian, P.; Gkitsas, V.; Carlucci, A.; Stalidis, P.; Albanis, G.; Karakottas, A.; Semertzidis, T.; Daras, P.; Giannitto, C.; et al. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. Int. J. Environ. Res. Public Health 2021, 18, 2842. https://doi.org/10.3390/ijerph18062842
Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, Karakottas A, Semertzidis T, Daras P, Giannitto C, et al. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. International Journal of Environmental Research and Public Health. 2021; 18(6):2842. https://doi.org/10.3390/ijerph18062842
Chicago/Turabian StyleChatzitofis, Anargyros, Pierandrea Cancian, Vasileios Gkitsas, Alessandro Carlucci, Panagiotis Stalidis, Georgios Albanis, Antonis Karakottas, Theodoros Semertzidis, Petros Daras, Caterina Giannitto, and et al. 2021. "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment" International Journal of Environmental Research and Public Health 18, no. 6: 2842. https://doi.org/10.3390/ijerph18062842