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Related Experiment Video

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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.

STAR Protocols
|November 29, 2025
PubMed
Summary

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:
bioinformaticsbiotechnology and bioengineeringcomputer sciencesmicroscopysingle 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.