This invention proposes an intelligent segmentation method for
reticular fiber stained
pathological images and its application. Addressing the limitations of existing technologies in identifying
reticular fiber structure disruptions to define tumor boundaries and artifacts caused by sliding windows, this invention employs adaptive sliding window segmentation and multi-stage pre-filtering to extract effective image blocks. The input is a dual-
stream network, where structural and visual flows are fused through cross-attention based on tissue physical size mapping receptive fields to achieve collaborative
verification of visual abnormalities and structural disruptions. Model training utilizes a dynamic boundary-aware loss term with physical scale constraints for joint optimization. Finally, a spatial weight
fusion mechanism combined with a
graph model incorporating physical priors eliminates breakage artifacts and smooths global boundaries. This invention is primarily used for high-fidelity
lesion segmentation of pituitary
neuroendocrine tumors.