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.
Montaha et al. (Mon,) studied this question.