Neonatal Brain MRI Image Segmentation Using U-Net With Enhanced Edge Detection Layers
Bibliographic record
Abstract
Due to low contrast and rapid development of immature brain tissues, MRI brain segmentation for infant brains is a more challenging task than adult brain MRI segmentation. For this research, enhanced edge detection layers were implemented in a U-Net framework by using guided filter modules (GFM). Ablation experiments were done to see the effectiveness of the modules in atlas-based 6-month infant MRI brain segmentation. This paper focused on the initial evaluation of the proposed network using the iSeg19 training and validation dataset from the Medical Image Computing and Computer Assisted Intervention (MICCAI) challenge. Dice coefficient, modified Hausdorff distance (MHD) and ASD were measured for each brain region using the variations of the model: (i) vanilla U-Net, (ii) single-edge detection layer with U-Net, (iii) U-Net with GFMs during the training and validation process. The benchmark model used for comparison is the latest model listed in the MICCAI iSeg challenge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".