Registration-based 3D Light Sheet Fluorescence Microscopy and 2D histology image fusion tool for pathological...

Histological analysis traditionally relies on thin tissue sections, providing inherently two-dimensional (2D) information. However, this approach captures only a fraction of the entire sample and lacks the spatial context necessary for comprehensive tissue assessment. Recent advancements in multimodal imaging have introduced the fusion of histological data with three-dimensional (3D) imaging techniques, such as Light Sheet Fluorescence Microscopy (LSFM), to enhance tissue analysis by integrating complementary spatial information. A key challenge in this fusion process is the accurate alignment of corresponding structures across modalities, which is complicated by differences in resolution, sectioning-induced deformations, and varying imaging orientations. This is further complicating in the case of 2D-to-3D registration where the initial alignment of the image inside the volume is unknown and registration processes are computationally expensive due to six degrees of freedom in the placement. Here, existing solutions often require manual selection of image pairs, fiducial markers or technical expertise, limiting accessibility to non-specialist users. To address these limitations, we introduce LitSHi (Light Sheet meets Histology), a novel registration tool that enables the automated and precise alignment of LSFM and histological images. LitSHi allows multimodal image fusion to be performed fully automatically, which significantly reduces the need for manual intervention. Using testicular tumor and pancreatic specimens, we evaluated LitSHi and demonstrated its ability to enhance structural correspondence between LSFM and histological images. The automated registration process markedly improved both efficiency and alignment accuracy compared with conventional manual or semi-automated approaches. Overall, LitSHi holds significant potential to advance digital pathology by enabling optimized multimodal tissue analysis and supporting future developments in computational pathology and AI-driven diagnostics.