Master thesis : New Cytomine modules for multimodal studies and mass spectrometry imaging
|Master thesis : New Cytomine modules for multimodal studies and mass spectrometry imaging
|Translated title :
|[fr] Nouveau module Cytomine pour les études multimodales et d'imagerie par spectrométrie de masse
|Date of defense :
|Committee's member(s) :
|Number of pages :
[en] mass spectrometry imaging
[en] template matching
[en] machine learning
[en] image registration
|Engineering, computing & technology > Computer science
|Target public :
Professionals of domain
|Université de Liège, Liège, Belgique
|Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
|Master thesis of the Faculté des Sciences appliquées
[en] Multimodal imaging analyses are large scale work, combining experience from many professionals in different disciplines, providing different modalities (i.e. data produced by an experiment) linked together.
The growth in multimodal analyses induces a demand for software to make some workflows possible, or help automate some other workflows, at lest partially.
Mass Spectrometry Imaging (MSI), although more than 50 years old, continues to see some development in the data processing domain, particularly with machine learning and deep learning applications, where some approaches tackle the preprocessing and the analysis of MSI datasets.
The analysis performed on MSI data greatly contributes from multimodal studies, providing a spatial distribution for the molecular content of the sample, thus adding valuable information to the study.
Multimodal analyses currently lack an open, collaborative web platform : such tools would allow for a greater share of experience thanks to the collaborative aspect, enable reproducibility because the analyses would run in the cloud, always on the same hardware, and the results would be available to all.
Such tools are being developed : Cytomine aims to add more effective multimodal tools to improve its capabilities, but integrating MSI data is not trivial.
The analysis of MSI data is not an easy task : file formats for this kind of data are abundant, but often vendor specific. imzML is an open effort to unify all these formats, which is supported by many pieces of software already. However, imzML is not the most appropriate format as its structure is very different from most imaging data format, making it ill-suited for visualization applications such as in Cytomine.
This master's thesis introduce a new, versatile and open format based on OME-Zarr, which is suitable for many modalities, including MSI. This file format is benchmarked against imzML to show its potential in server applications, such as Cytomine.
In addition to the new file format, the developed pieces of software includes a convertor from imzML and some preprocessing tools designed for the file format. Using the developed file format, a machine learning workflow classifies spectra from a multimodal dataset with label coming from other modalities, and provide a list of important features as a mean of interpretation.
While these pieces of software are currently developed to be run on a local machine, they lay the ground for cloud based application that can be integrated with Cytomine.
Cite this master thesis
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