Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI
Why this work is in the frame
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Bibliographic record
Abstract
Intracranial volume (ICV) segmentation, also known as brain extraction or skull-stripping, is a critical preprocessing step in analytical pipelines for studying neurodegenerative diseases in magnetic resonance imaging (MRI). While the fluid-attenuated inversion recovery (FLAIR) MRI modality has emerged as an important sequence for analyzing cerebrovascular and neurodegenerative disease, most existing automated ICV segmentation methods have been developed for T1-weighted or multi-modal inputs. Additionally, many methods have been designed using single centre data of healthy subjects and encounter difficulties using images with varying acquisition parameters and neurodegenerative pathology. In this work, we develop and evaluate 2 traditional and 8 deep learning algorithms for ICV segmentation in FLAIR MRI. Training and testing were completed on 175 vol (8317 images) from 2 dementia and 1 vascular disease cohort. A human phantom FLAIR MRI dataset from a repeatedly scanned, healthy individual was also utilized for reliability analysis. Images were acquired from 47 imaging centres with varying scanners and parameters. To measure and compare performance, we present a novel framework for evaluating the effectiveness of computer generated segmentations on multicentre datasets. The evaluation framework includes assessments of algorithm accuracy, generalization capabilities, robustness to pathology and spatial location, and volumetric measurement reliability - all important dimensions for establishing proof of effectiveness (a prerequisite to clinical translation). The top performing method was a multiple resolution U-Net (MultiResUNet), which achieved a mean Dice similarity coefficient greater than 98% and was robust across pathology levels and spatial locations. Our results confirm a FLAIR-based ICV analytical pipeline can alone be utilized for large-scale neurodegenerative disease research. The presented evaluation framework can be deployed by other researchers to assess the viability of tools proposed for automated analysis of diverse, clinical MRI datasets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it