Automated Targeted Sectioning of Resin-Embedded Hard Tissue Specimen Using Micro-Computed Tomography in Combination With Laser Microtomy.

Abstract

Histological analysis of hard tissue specimens is widely used in clinical practice and preclinical research, but it remains a labor-intensive and destructive process. In particular, resin-embedded tissues present challenges due to the inability to target regions of interest (ROI), as internal structures are not visible externally. This work proposes a guided sectioning workflow that enables precise targeting of concealed ROIs using a multimodal approach. By combining microCT imaging with an automated cutting system, and laser microtomy, precise targeted sectioning was achieved. MicroCT imaging enables visualization of internal structures, guiding the automated cutting system for precise sectioning. Laser microtomy then allows thin tissue sections to be prepared while preserving diagnostic features. Comparing the automated workflow to the conventional cutting-grinding technique showed that the new method improved accuracy by a factor of 7 and reduced material loss by half and processing time by 75%. Validation was performed by comparing the histological sections with in silico target planes generated from the microCT scans, showing precise alignment between the targeted regions and the prepared sections. We demonstrate that the proposed approach significantly reduces tissue loss and offers a more efficient workflow compared to traditional methods. Additionally, microCT-based targeting enables accurate correlation between histological findings and 3D pathological structures. The automated guided sectioning workflow provides valuable insights into tissue pathology, enhancing clinical diagnostics and preclinical research. It also facilitates the generation of multimodal datasets, which can be used in future machine learning applications.