Reliability assessment of tissue classification algorithms for multi‐center and multi‐scanner data
Bibliographic record
Abstract
Abstract Background Gray and white matter volumes are important imaging markers of pathology and disease progression. Such measures are usually estimated from tissue segmentation maps produced by image processing pipelines. However, the reliability of the produced segmentations when using multi‐center and multi‐scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different scanners models and sites. Method Data included 90 T1‐weighted images of a single individual, scanned in 73 sessions across 27 sites (Duchesne et al., 2019). An average T1‐weighted image template was created out of all scans (Fonov et al., 2009). For each algorithm, their respective segmentation masks on this template was used as a silver standard (Figure 1). Dice Kappa similarity scores between these silver standards and individual segmentations and variability in tissue volumes across segmentations were assessed for Atropos (Avants et al., 2011); BISON (Dadar and Collins, 2019); Classify_Clean (Cocosco et al., 2003); FAST5.0 (Zhang et al., 2001); FreeSurfer6.0.0 (Fischl, 2012); and SPM12 (Penny et al., 2011). We also estimated the sample size necessary to detect a significant 1% volume reduction based on the variability of the estimates from each method within and across scanner models (80% power, 2‐tailed significance). Result Across tissue types, BISON had the lowest overall variability in Dice Kappa, followed by SPM12 (Figure 2). For GM, BISON had the highest overall Dice Kappa (0.94±0.01), followed by SPM12 (0.93±0.01) and Atropos (0.92±0.03); while Atropos had the highest overall Dice Kappa for WM (0.94±0.02), followed by BISON (0.93±0.01) and SPM12 (0.93±0.01). BISON had the lowest overall variability in its volumetric estimates (GM:57.60±0.4, WM:34.49±0.4, CSF:7.90±0.3), followed by FreeSurfer (GM: 49.80±0.7, WM: 39.52±0.8, CSF: 10.71±1.2), and SPM12 (GM:57.76±1.3, WM:33.08±1.1, CSF:9.12±0.9, Figure 3). BISON also had the smallest sample size requirement across all scanners and tissue types, followed by FreeSurfer, and SPM12 (Table 1). As expected, the necessary sample sizes decreased when using data from a specific scanner. Conclusion Our comparisons provide a benchmark on the reliability of currently used tissue classification techniques and the amount of variability that can be expected when using large multi‐center and multi‐scanner databases.
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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 | 0.074 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".