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Related Experiment Video

Updated: Jan 10, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Protocol to annotate and automate single-cell instance segmentation on stimulated Raman histology using deep

Abhishek Bhattacharya¹, Eric Landgraf², Cheng Jiang²

¹University of Michigan, Ann Arbor, MI 48109, USA; NYU Langone, New York, NY 10016, USA.

|November 29, 2025

Summary

This summary is machine-generated.

This study introduces a method for automated cell segmentation in label-free stimulated Raman histology (SRH) images using artificial intelligence. The developed protocol enables single-cell spatial analysis for neurosurgical applications.

Keywords:

bioinformatics biotechnology and bioengineering computer sciences microscopy single cell

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Area of Science:

Biomedical Optics
Computational Pathology
Artificial Intelligence in Medicine

Background:

Stimulated Raman histology (SRH) offers label-free, high-resolution molecular imaging without staining.
Intra-operative neurosurgical imaging requires rapid and accurate cellular analysis.
Automated segmentation is crucial for quantitative analysis of complex tissue microenvironments.

Purpose of the Study:

To develop a protocol for cell labeling and AI model training for automated cell segmentation in intra-operative SRH neurosurgical images.
To enable single-cell spatial analysis on SRH data.
To provide tools for efficient annotation and analysis of SRH images.

Main Methods:

Protocol for cell labeling compatible with SRH imaging.
Training of AI models for automated cell segmentation on intra-operative SRH data.
Utilized ELUCIDATE (web-based SRH cell annotation tool) and DetectSRH (Python library) for analysis.

Main Results:

Successful implementation of a protocol for cell labeling and AI-driven segmentation on SRH images.
Demonstrated feasibility of single-cell spatial analysis using the developed tools.
SRH images acquired intra-operatively from neurosurgical cases were effectively analyzed.

Conclusions:

The presented protocol and tools facilitate automated cell segmentation and single-cell spatial analysis in SRH neurosurgical imaging.
This approach enhances the utility of label-free optical imaging for intra-operative decision-making.
Future applications may include real-time tissue characterization during surgery.