BISON: Brain tissue segmentation pipeline using T <sub>1</sub> ‐weighted magnetic resonance images and a random forest classifier
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Tissue segmentation from T 1 ‐weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI. Methods BISON was developed and cross‐validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test–retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state‐of‐the‐art commonly used tissue classification method from advanced normalization tools (ANTs). Results BISON cross‐validation dice kappa values against manual segmentations of 72 MRI volumes yielded κ GM = 0.88, κ WM = 0.85, κ CSF = 0.77, outperforming Atropos (κ GM = 0.79, κ WM = 0.84, κ CSF = 0.64), test–retest values on 20 subjects of κ GM = 0.94, κ WM = 0.92, κ CSF = 0.77 outperforming both manual (κ GM = 0.92, κ WM = 0.91, κ CSF =0.74) and Atropos (κ GM = 0.87, κ WM = 0.92, κ CSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs. Conclusion BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it