BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier
Bibliographic record
Abstract
Abstract Introduction Accurate differentiation of brain tissue types from T1-weighted magnetic resonance images (MRIs) is a critical requirement in many neuroscience and clinical applications. Accurate automated tissue segmentation is challenging due to the variabilities in the tissue intensity profiles caused by differences in scanner models and acquisition protocols, in addition to the varying age of the subjects and potential presence of pathology. In this paper, we present BISON (Brain tISue segmentatiON), a new pipeline for tissue segmentation. Methods BISON performs tissue segmentation using a random forests classifier and a set of intensity and location priors obtained based on T1-weighted images. The proposed method has been developed and cross-validated based on multi-center and multi-scanner manual labels of 72 subjects aging from 5-96 years old, ensuring the generalizability of the results to new data from various age ranges. In addition, we assessed the test-retest reliability of BISON on 2 datasets; a. using 20 subjects that had scan/re-scan MRIs and manual segmentations available, and b. using a human phantom dataset including 90 scans from a single individual acquired across 10 years. Results The results of the proposed method were compared against Atropos, a commonly used tissue classification method from ANTs. The proposed method yielded cross-validation Dice Kappa values of κ GM = 0.88 ± 0.03, κ WM = 0.85 ± 0.03, κ CSF = 0.77 ± 0.11, outperforming ANTs Atropos (κ GM = 0.79 ± 0.05, κ WM = 0.84 ± 0.05, κ CSF = 0.64 ± 0.22) as well as test-retest Dice Kappa values of κ GM = 0.94 ± 0.006, κ WM = 0.92 ± 0.006, κ CSF = 0.77 ± 0.11 outperforming both manual (κ GM = 0.92 ± 0.01, κ WM = 0.91 ± 0.01, κ CSF = 0.74 ± 0.03) and ANTs Atropos (κ GM = 0.87 ± 0.001, κ WM = 0.92 ± 0.001, κ CSF = 0.79 ± 0.05). The human phantom dataset validations showed high generalizability for both Atropos (κ GM = 0.97 ± 0.01, κ WM = 0.96 ± 0.01, κ CSF = 0.93 ± 0.02) and BISON (κ GM = 0.95 ± 0.01, κ WM = 0.94 ± 0.01, κ CSF = 0.85 ± 0.03), while Atropos tended to consistently under-segment the cortical CSF. Finally, our assessment of BISON, Atropos, FAST from FSL, and SPM12 segmentations in presence of white matter hyperintensities (WMHs) showed that BISON outperforms the other three methods, correctly detecting WMHs as WM. Conclusion Our results show that BISON can provide accurate and robust segmentations in data from different age ranges and various scanner models, making it ideal for performing tissue classification in 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".