Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Bibliographic record
Abstract
Purpose To present a method that automatically segments and quantifies abnormal CT patterns commonly present in COVID-19, namely ground-glass opacities and consolidations. Materials and Methods In this retrospective study, the proposed method takes as input a noncontrast chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (percentage of opacity, percentage of high opacity [PO, PHO]) is global, while the second of (lung severity score, lung high opacity score [LSS, LHOS]) is lobe-wise. Evaluation of the algorithm is reported on CT studies of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe, and the United States collected between 2002 and April 2020. Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. Results Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO (P < .001), 0.91 for LSS (P < .001), and 0.90 for LHOS (P < .001). Ninety-eight of 100 healthy controls had a predicted PO of less than 1%; two had between 1% and 2%. Automated processing time to compute the severity scores was 10 seconds per case compared with 30 minutes required for manual annotations. Conclusion A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores. Keywords: CT, Lung, Segmentation/Vision/Application Domain, Quantification/Vision/Application Domain, Supervised Learning, Reinforcement Learning © RSNA, 2021 Supplemental material is available for this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".