Histological staining techniques offer complementary insights into tissue architecture and molecular expression. Co-registering images from the same section enables the integration of morphological and molecular features, thereby enhancing pathological diagnosis. However, achieving precise alignment remains challenging due to unreliable feature extraction or excessive degrees of freedom in current tools. To address this, we present the Registration-Based Multimodal Image Calculator (RBMIC), a software designed for accurate registration of immunofluorescence (IF) and hematoxylin and eosin (H&E) images from sequentially stained tissue sections. RBMIC employs a landmark-driven framework that adaptively selects between nonlinear and linear registration algorithms to accommodate varying landmark counts and deformation complexity. By incorporating quantitative quality assessment and a hierarchical fault-tolerant mechanism, our method achieves robust, high-accuracy cross-modality registration. Applied to pathological characterization, RBMIC reveals that Down Syndrome is associated with impaired development of specific slow-twitch muscle fibers, suggesting a link to observed muscle dysfunction.
Fu et al. (Mon,) studied this question.