Machine-learning Assisted Profiling of Homologous Recombination (MAP-HR)

For the Publication titled: Automated Machine Learning Profiling with MAP-HR for Quantifying Homologous Recombination Foci in Patient Samples. (DOI: 10.1101/2025.02.20.639329) To use: put each of the .ijm files within the same parent folder. Execute by installing the MAP_HR-Segmentation-Foci_Feature_extraction.ijm within FIJI (Image J), and starting the macro by pressing "M". The Primary .ijm is the major pipeline, which calls the additional 2 .ijm files specifically to: 1. Organize and re-label input.czi images by corresponding channels which match MAP-HR datastructure, and 2. Open roi-selector macro, which enables user to draw tumor-regions for analysis in uploaded images. Note: Microscope and user-application specific segmentation models and imaging channels must be checked and trained prior to utilizing MAP-HR.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-30
DOI
https://doi.org/10.5281/zenodo.15771983
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Machine-learning Assisted Profiling of Homologous Recombination (MAP-HR)

Zenodo (CERN European Organization for Nuclear Research)
Radiomics and Machine Learning in Medical Imaging
article

Machine-learning Assisted Profiling of Homologous Recombination (MAP-HR)

article en

Abstract

For the Publication titled: Automated Machine Learning Profiling with MAP-HR for Quantifying Homologous Recombination Foci in Patient Samples. (DOI: 10.1101/2025.02.20.639329) To use: put each of the .ijm files within the same parent folder. Execute by installing the MAP_HR-Segmentation-Foci_Feature_extraction.ijm within FIJI (Image J), and starting the macro by pressing "M". The Primary .ijm is the major pipeline, which calls the additional 2 .ijm files specifically to: 1. Organize and re-label input.czi images by corresponding channels which match MAP-HR datastructure, and 2. Open roi-selector macro, which enables user to draw tumor-regions for analysis in uploaded images. Note: Microscope and user-application specific segmentation models and imaging channels must be checked and trained prior to utilizing MAP-HR.

Zenodo (CERN European Organization for Nuclear Research)
Oregon Health & Science University (US)
Openalex Percentile: Top 43%
Radiomics and Machine Learning in Medical Imaging
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