Introduction
Material and Methods
Datasets
Dataset name | Lung | GGO | CT-SS | N. of |
---|---|---|---|---|
mask | mask | cases | ||
Plethora [8] | Yes | No | No | 402 |
Lung CT Segmentation Challenge [15] | Yes | No | No | 60 |
COVID-19 Challenge [1] | No | Yes | No | 199 |
MosMed [12] | No | No | No | 1110 |
MosMed (annotated subsample) | No | Yes | Inferable | 50 |
MosMed (in-house annotated subsample) | Yes | No | No | 91 |
COVID-19-CT-Seg [11] | Yes | Yes | Inferable | 10 |
LungQuant: a DL based quantification analysis pipeline
U-net
The U-net cascade for lesion quantification and severity score assignment
Training details and evaluation strategy for the U-nets
Cross-validation strategy
U-net\(_1\) | Train | Val | Test |
---|---|---|---|
Plethora | 319 | 40 | 40 |
MosMed (91 CT-0) | 55 | 18 | 18 |
LCTSC | 36 | 12 | 12 |
COVID-19-CT-Seg | – | – | 10 |
U-net\(_2^{60\%}\) | Train (60%) | Val (20%) | Test |
---|---|---|---|
COVID-19 Challenge | 119 | 40 | 40 |
MosMed (50 CT-1) | 30 | 10 | 10 |
COVID-19-CT-Seg | – | – | 10 |
U-net\(_2^{90\%}\) | Train (90%) | Val (10%) | Test |
---|---|---|---|
COVID-19 Challenge | 179 | 20 | – |
MosMed (50 CT-1) | 45 | 5 | – |
COVID-19-CT-Seg | – | – | 10 |
Results
U-net\(_1\): Lung segmentation performance
Test set | Masks of U-net size | Masks before refinement | Masks after refinement |
---|---|---|---|
vDSC | vDSC | vDSC | |
Plethora | 0.96 ± 0.02 | 0.95 ± 0.02 | 0.95 ± 0.04 |
MosMed | 0.97 ± 0.02 | 0.97 ± 0.02 | 0.97 ± 0.02 |
LCTSC | 0.96 ± 0.03 | 0.95 ± 0.03 | 0.96 ± 0.01 |
COVID-19-CT-Seg | 0.96 ± 0.01 | 0.95 ± 0.01 | 0.95 ± 0.01 |
U-net\(_2\): COVID-19 lesion segmentation performance
U-net | Trained on | Test set | U-net size | Original CT size |
---|---|---|---|---|
(vDSC) | (vDSC) | |||
U-net\(_2^{60\%}\) | COVID-19 challenge | COVID-19 challenge | 0.51 ± 0.24 | 0.51 ± 0.25 |
COVID-19 Challenge | MosMed | 0.39 ± 0.19 | 0.40 ± 0.19 | |
MosMed | MosMed | 0.54 ± 0.22 | 0.55 ± 0.22 | |
MosMed | COVID-19 challenge | 0.25 ± 0.23 | 0.25 ± 0.23 | |
COVID-19 challenge | COVID-19 challenge | 0.49 ± 0.21 | 0.50 ± 0.21 | |
+ MosMed | + MosMed | |||
U-net\(_2^{90\%}\) | COVID-19 challenge | COVID-19 challenge | 0.64 ± 0.23 | 0.65 ± 0.23 |
+ MosMed | + MosMed |
Metrics | Lung segmentation | |||
---|---|---|---|---|
vDSC | sDSC (1 mm) | sDSC (5 mm) | sDSC (10 mm) | |
LungQuant (U-net\(_2^{60\%})\) | 0.96 ± 0.01 | 0.66 ± 0.09 | 0.95 ± 0.02 | 0.98 ± 0.01 |
LungQuant (U-net\(_2^{90\%})\) | 0.95 ± 0.01 | 0.65 ± 0.09 | 0.95 ± 0.02 | 0.98 ± 0.01 |
Infection Segmentation | ||||
LungQuant (U-net\(_2^{60\%})\) | 0.62 ± 0.09 | 0.29 ± 0.06 | 0.75 ± 0.11 | 0.90 ± 0.09 |
LungQuant (U-net\(_2^{90\%})\) | 0.66 ± 0.13 | 0.36 ± 0.13 | 0.76 ± 0.18 | 0.87 ± 0.13 |
Evaluation of the quantification performance of the LungQuant system on a completely independent set
Evaluation of lung and COVID-19 lesion segmentations
Percentage of affected lung volume and CT-SS estimation
U-net | Dataset | Accuracy | Misclassified | Misclassified |
---|---|---|---|---|
by 1 class | by 2 classes | |||
U-net\(_2^{60\%}\) | COVID-19-CT-Seg | 6/10 | 4/10 | 0 |
U-net\(_2^{90\%}\) | COVID-19-CT-Seg | 9/10 | 1/10 | 0 |