Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it