Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation main @ bd51f80
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Source codes for manuscript "Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation"
"ExampleWorkflow.ipynb" is a methods document to demonstrate the workflow of our multimodal fusion-based spatial segmentation.
"Utilities.py" contains all the tools to implement our method.
"gui.py" and "registration_gui.py" are files to implement linear and nonlinear registration.
(Licence: GPL-3)
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main @ bd51f80 (earliest) Created 16th Feb 2023 at 00:34 by Ang Guo
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Created: 16th Feb 2023 at 00:34
Last updated: 8th Mar 2023 at 23:57
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Mass spectrometry imaging, which localizes molecules in a tag-free, spatially-resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate Regions-of-Interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI ...
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Public web page: https://github.com/guoang4github/ROIforMSI/
