Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT Scans
Bibliographic record
Abstract
Consistent segmentation of CT scans in COVID-19 patients across multiple time points is important to accurately evaluate disease progression and therapeutic response. In medical domains, previous interactive segmentation studies have been mainly conducted on data from a single time point. However, the valuable segmentation information from previous time points is often underutilized in assisting the segmentation of a patient’s follow-up scans. Moreover, fully automatic segmentation techniques frequently produce results that would need further refinement for clinical applicability. In this study, we propose a novel single-network model for interactive segmentation that fully leverages all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes concatenated slices of 3D volumes from two-time points (target and reference), employing the segmentation results from the reference time point as a guide for segmenting the target scan. Subsequent refinement rounds incorporate user feedback in the form of scribbles that rectify the segmentation, in addition to incorporating the previous segmentation results of the target scan. This iterative process ensures the preservation of segmentation information from prior refinement rounds. Experimental results obtained from our in-house multiclass longitudinal COVID-19 dataset demonstrate the effectiveness of the proposed method compared to its static counterpart, thus providing valuable assistance in localizing COVID-19 infections in patients’ follow-up scans.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".