Misssegmentation

Misssegmentation refers to errors in segmenting an image, signal, or volume where regions of interest are not correctly identified or delineated. It describes failures to detect all relevant areas or to separate adjacent structures with accurate boundaries, resulting in missing segments, merged regions, or misplaced borders. Misssegmentation is discussed across fields such as computer vision, medical imaging, and remote sensing, where pixel- or voxel-level labeling is used for analysis and decision making.

In computer vision, misssegmentation can occur in semantic, instance, or panoptic segmentation tasks. It manifests as false negatives (parts of an object are not segmented), undersegmentation (distinct objects merged into a single segment), or boundary errors (inaccurate edges). In medical imaging, misssegmentation may involve failing to delineate an organ, lesion, or tumor, potentially impacting diagnosis, treatment planning, or monitoring. Causes include occlusion, low contrast between foreground and background, noise, artifacts, motion blur, and limited resolution. Algorithmic factors such as insufficient training data, class imbalance, domain shift, annotation noise, and loss functions that do not align with evaluation metrics can also contribute.

Analysis and evaluation typically use metrics like Intersection over Union (IoU), the Dice coefficient, precision, recall, and boundary-focused measures. Misssegmentation lowers these metrics and can affect downstream tasks such as object counting, tracking, or quantitative analysis.

Mitigation approaches include improving data quality and diversity, employing multi-scale or context-aware models, data augmentation, and refined loss functions. Post-processing, ensemble methods, uncertainty estimation, and human-in-the-loop validation are also used to reduce misssegmentation and improve reliability.