Workflow Type: Jupyter
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Segmentation and Reference Point Detection for Laser Capture Microdissection (LMD)

Project Summary

This repository contains the code for a Cellpose-SAM & pyLMD project dedicated to automating cell boundary and reference point detection in microscopic images used for Laser Capture Microdissection (LMD).

The primary function of this repository is to identify the boundaries of target cells and detect precise reference points from laser engraved 'T' structures and is designed to enhance the reproducibility of the LMD workflow.

Getting Started

📋 Prerequisites

This project exclusively uses the Pixi package manager to guarantee a reliable and isolated Python environment. Install Instructions

💻 Installation and Setup

  1. Clone the repository:

    git clone https://github.com/Immunodynamics-Engel-Lab/lmd-imageanalysis.git
    cd lmd-imageanalysis
    
  2. Initialize the Pixi Environment:

    Pixi reads the required dependencies from the pixi.toml file and creates a ready-to-use virtual environment.

    pixi install
    

📊 FAIR compliance

Example data for testing the in silico workflow is available on Zenodo

The workflow is available through Zenodo and WorkflowHub

All resources are publicly accessible and distributed under open licenses where applicable.


✉️ Correspondence

Prof. Dr. Daniel R. Engel: Department of Immunodynamics, Institute of Experimental Immunology and Imaging, University Hospital Essen, Essen, Germany

Version History

v.1.0.3 (latest) Created 13th May 2026 at 18:13 by Devon Siemes

fix: general doi for Zenodo


Frozen v.1.0.3 bfd7c27

v1.0.1 (earliest) Created 7th May 2026 at 10:57 by Devon Siemes

docs: add example data & correspondence


Frozen v1.0.1 e887c5c
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Citation
Siemes, D., & Engel, D. R. (2026). {lmd-imageanalysis} (Version 1.0.2). https://doi.org/10.5281/zenodo.20067579
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Views: 403   Downloads: 74

Created: 7th May 2026 at 10:57

Last updated: 13th May 2026 at 18:13

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