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Updated: May 24, 2025

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

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Published on: June 3, 2022

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

P Nolte, C J Groger, C Frey

IEEE Transactions on Bio-Medical Engineering

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces an automated workflow for precise tissue sectioning, improving accuracy sevenfold and reducing waste. This method enhances histological analysis for better diagnostics and research.

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Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Published on: October 17, 2018

Area of Science:

Biomedical Engineering
Histopathology
Medical Imaging

Background:

Histological analysis of hard tissues is crucial but labor-intensive and destructive.
Resin-embedded tissues pose challenges for targeting internal structures due to lack of external visibility.

Purpose of the Study:

To develop a guided sectioning workflow for precise targeting of concealed regions of interest (ROIs) in hard tissue specimens.
To overcome limitations of conventional histological analysis methods.

Main Methods:

A multimodal approach combining micro-computed tomography (microCT) imaging with an automated cutting system and laser microtomy.
MicroCT imaging for internal structure visualization to guide automated sectioning.
Laser microtomy for preparing thin, diagnostically preserved tissue sections.

Main Results:

The automated workflow demonstrated a 7-fold improvement in accuracy compared to conventional methods.
Material loss was halved, and processing time was reduced by 75%.
Validation confirmed precise alignment between targeted regions and prepared histological sections.

Conclusions:

The proposed automated guided sectioning workflow significantly reduces tissue loss and processing time.
It enables accurate correlation between histological findings and 3D pathological structures.
This method enhances clinical diagnostics, preclinical research, and facilitates multimodal dataset generation for machine learning.