Beyond Accuracy: A Critical Look at Traditional Model Evaluation in Segmentation

Traditional model evaluation in segmentation heavily emphasizes objective metrics, primarily focusing on the accuracy of segmentation results relative to a 'ground truth'. This approach typically involves calculating the correlation coefficient between the segmentation output and the pre-defined reference standard. While providing a quantifiable measure of similarity, this method may not fully encapsulate the nuances of segmentation performance, potentially overlooking crucial aspects such as spatial consistency, boundary adherence, and the impact of noise or artifacts.