用预训练模型实现高效脑区细胞架构分割，仅需少量标注即可达到高精度。

Cytoarchitectonic mapping provides anatomically grounded parcellations of brain structure and forms a foundation for integrative, multi-modal neuroscience analyses. These parcellations are defined based on the shape, density, and spatial arrangement of neuronal cell bodies observed in histological imaging. Recent works have demonstrated the potential of using deep learning models toward fully automatic segmentation of cytoarchitectonic areas in large-scale datasets, but performance is mainly constrained by the scarcity of training labels and the variability of staining and imaging conditions. To address these challenges, we propose an interactive cytoarchitectonic parcellation framework that leverages the strong transferability of the DINOv3 vision transformer. Our framework combines (i) multi-layer DINOv3 feature fusion, (ii) a lightweight segmentation decoder, and (iii) real-time user-guided training from sparse scribbles. This design enables rapid human-in-the-loop refinement while maintaining high segmentation accuracy. Compared with training an nnU-Net from scratch, transfer learning with DINOv3 yields markedly improved performance. We also show that features extracted by DINOv3 exhibit clear anatomical correspondence and demonstrate the method's practical utility for brain region segmentation using sparse labels. These results highlight the potential of foundation-model-driven interactive segmentation for scalable and efficient cytoarchitectonic mapping.