Balanced-Boundary IoU: A holistic algorithm towards improving segmentation evaluation metric
Bibliographic record
Abstract
Image segmentation is a fundamental task in computer vision. Precise evaluation metrics are essential for assessing the performance of segmentation models, particularly in medical imaging. Intersection over Union (IoU) is commonly used to evaluate the performance of segmentation models. However, it has been reported to be biased based on object size, placing less emphasis on boundary regions. To address this issue, alternative IoU-based measures have been proposed, which are either asymmetric or completely ignore the inner regions of objects. In this study, we propose a Balanced Boundary IoU (BBIoU) to overcome the limitations of these metrics and provide a more accurate assessment of both the boundary and inner region of objects. BBIoU is a symmetric function that considers both the inner regions and boundaries of objects, providing a comprehensive measure for evaluating segmentation models. We evaluated BBIoU across six different medical imaging datasets for binary segmentation, covering a wide range of object sizes and shapes. Additionally, extensive analysis of synthetic and real predictions demonstrated that BBIoU is robust and consistent while avoiding biases such as erroneous penalization and sensitivity to object size. This study presents a comprehensive comparative analysis of IoU, alternative IoU-based metrics, and BBIoU, demonstrating the suitability of BBIoU for evaluating segmentation quality across diverse image segmentation tasks.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".