Segmentation as Editing: Unified Medical Optical Image Segmentation with Differentiable Edit-Space Geometry Adaptation

Medical optical imaging is widely used in clinical diagnosis and pathological assessment. Precise segmentation of clinically meaningful anatomical structures and pathological regions enables quantitative assessment for clinical decision-making and research. However, this field still faces at least two challenges: the lack of a unified segmentation dataset dedicated to diverse optical modalities and the need for a unified segmentation model that achieves competitive performance across multiple optical imaging modalities. To this end, we construct OptiMedEdit-37K, a segmentation-centered medical optical image editing dataset covering 7 medical optical imaging modalities and 18 anatomical and pathological targets. Meanwhile, we develop Segmentation as Editing as a unified paradigm for medical optical image segmentation. Through staged fine-tuning on OptiMedEdit-37K under this paradigm, we first establish a Unified Medical Optical image segmentation model (UniMedOpti). To retain its shared capabilities while better modeling target-specific geometric structures, we then augment UniMedOpti with Geometry-aware Residual Adaptation through Differentiable Edit-space Alignment (GRADE). It freezes the shared backbone and trains low-rank residuals using geometric losses on soft masks differentiably recovered from generated edits. UniMedOpti achieves competitive segmentation performance across multiple medical optical imaging modalities and datasets. The experimental results demonstrate that Segmentation as Editing combines cross-modal generality with competitive accuracy.