Many image segmentation techniques generate sequences of region partitions by tuning parameters that control the level of detail or abstraction, but sequences often violate hierarchical consistency, where each region at one level should be entirely contained within a region at the next. Such inconsistencies undermine the benefits of hierarchical models in multiscale analysis, such as structural coherence and computational efficiency. Despite the importance of hierarchy, existing methods often overlook how to detect and correct violations in segmentation sequences. In this work, we propose a framework for identifying and classifying partitions that most harm the hierarchy. We introduce a recursive measure to quantify the accumulated inconsistency within a sequence and present an optimization strategy that pinpoints and removes the most harmful partition. Experiments on several datasets demonstrate that our approach effectively improves the hierarchiness of the sequence by analyzing and refining the segmentation series.