Semi-automatic segmentation of the fetal brain from Magnetic Resonance Imaging
Bibliographic record
Abstract
Abstract Template-based segmentation techniques have been used for targeting deep brain structures in fetal MR images. In this study, two registration algorithms were compared to determine the optimal strategy of segmenting subcortical structures in T2-weighted images acquired during the third trimester of pregnancy. Adult women with singleton pregnancies (n=9) ranging from 35-39 weeks gestational age were recruited. Fetal MRI were performed on 1.5 T and 3 T scanners. Automatic fetal brain segmentation and volumetric reconstruction algorithms were performed on all subjects using the NiftyMIC software. An atlas of cortical and subcortical structures (36 weeks’ gestation) was registered into native space using ANTs (Automatic Normalization Tools) and FLIRT (FMRIB’s linear image registration tool). The cerebellum and thalamus were manually segmented. Dice coefficients were calculated to validate the reliability of automatic methods and to compare the performance between ANTs (nonlinear) and FLIRT (affine) registration algorithms compared to the gold-standard manual segmentations. The Dice-kappa values for the automated labels were compared using the Wilcoxon test. Comparing cerebellum and thalamus masks against the manually segmented masks, the median Dice-kappa coefficients for ANTs and FLIRT were 0.76 (interquartile range [IQR]= 0.56-0.83) and 0.65 (IQR=0.5-0.73), respectively. The Wilcoxon test (Z=4.9, P <0.01) indicated that the ANTs registration method performed better than FLIRT for the fetal cerebellum and thalamus. We found that a nonlinear registration method, provided improved results compared to an affine transformation. Nonlinear registration methods may be preferable for subcortical segmentations in MR images acquired in third-trimester fetuses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".