MassVision is an open-source tool built on the 3D Slicer ecosystem, making complex MSI workflows accessible without coding.
Amoon Jamzad, a postdoctoral fellow at the Medical Informatics (Med-i) Laboratory at Queen’s School of Computing, has launched Mass Vision a new open-source platform designed to simplify and expand access to Mass Spectrometry Imaging (MSI) data analysis.
Built on the 3D Slicer ecosystem, MassVision addresses a long-standing challenge in MSI research. While MSI can reveal rich molecular information within biological samples, existing analysis workflows are often complex and fragmented, requiring advanced programming skills. These barriers can limit both efficiency and accessibility for researchers.
MassVision offers a code-free, integrated environment that allows researchers to focus on biological discovery rather than software development. The platform supports high-dimensional and multi-slide MSI datasets, making it well-suited for both exploratory research and high-throughput studies.
MassVision provides a user-friendly set of tools designed to help researchers explore and understand MSI (Mass Spectrometry Imaging) data more intuitively. The platform allows users to visualize molecular patterns and organize and compare datasets easily. It also incorporates advanced analysis methods, including built-in AI tools, which can be utilized without any programming skills. MassVision has demonstrated its effectiveness in real-world research scenarios, aiding in the discovery of significant biological patterns that are often challenging to detect using traditional methods. Additionally, MassVision is open-source and freely accessible through the 3D Slicer platform.
Data Access: In-house MSI data used for validation is publicly available in Metabolites under identifier MTBLS12868.