Depth Encoding for Neonatal Patient Segmentation
Bibliographic record
Abstract
Patient segmentation is an important step in neonatal monitoring for subsequent applications including vital sign monitoring, jaundice detection, or clonic seizure detection. Many studies have faced difficulty in obtaining clear delineation between the patient and the background, instead settling for segmentation of specific body parts, segmentation of visible skin only, or manual selection of regions of interest. The outline of the full body, however, provides a more holistic detection of the entire patient which can be useful for various applications. This study investigates whole-body semantic segmentation of patients under varying levels of coverage (unclothed, clothed, and partially covered with blankets), in complex scenes from the neonatal intensive care unit, from patients placed across all bed types (crib, incubator, and overhead warmer), and in different subject poses (supine, prone, and fetal position). To improve the patient-background delineation, several depth encoding and RGB-D fusion techniques are investigated with a Mask R-CNN model. This study demonstrates how depth information can effectively enhance the RGB image for neonatal patient segmentation, especially when the delineation is unclear due to patient coverage. While RGB images provided suitable predictions for optimal “clear” contours, RGB-D fusion images were required to achieve accurate patient segmentation in challenging “unclear” contours with over 71% IOU and over 82% Dice metrics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".