Radiuma

Medical image computing software is essential for identifying imaging biomarkers that can support diagnosis, prognosis, treatment planning, and clinical research. However, the lack of standardized, user-friendly, and reproducible software environments has limited the broader adoption of advanced medical image analysis workflows. We present Radiuma, a freely available modular platform designed to support reliable and reproducible medical image analysis across multiple modalities and file formats. Radiuma integrates image reading, visualization, registration, fusion, processing, segmentation, radiomics feature extraction, and machine learning modules for classification, regression, and clustering. Its modular design allows users to execute each component independently or connect modules through a visual workflow system, where the output of one step can be graphically passed to the next. This enables the creation of custom, executable, and reproducible multi-step pipelines without requiring extensive programming expertise. Results from each module can be inspected directly in the visualization window, providing immediate feedback on processing quality and workflow accuracy. Radiuma also supports saving and sharing customized workflows, promoting transparency, reusability, and consistency across collaborative studies. By combining flexibility, usability, and standardized analysis tools, Radiuma provides a practical environment for radiomics and machine learning research in clinical and translational settings. The platform is designed to be accessible to users with diverse expertise, including radiologists, physicists, clinicians, and data scientists

We present Radiuma, an open-access, zero-code, and shareable workflow generator for medical image analysis and machine learning model development. Radiuma comprises multiple tools for 2D/3D image processing and machine learning, and supports a wide range of imaging modalities, including PET, SPECT, CT, MRI, Ultrasound (US), and Xray, with compatibility for various image formats such as DICOM, NIfTI, and NRRD. The integrated image viewer enables multi-planar visualization, and supports interactive segmentation and exporting segmentation in different formats, including RT-Struct. Moreover, Radiuma includes multiple image registration and fusion algorithms to facilitate multimodal image analysis. It also offers comprehensive image preprocessing options using various filters, and supports advanced radiomics analysis, extracting radiomics features including shapes, intensity, texture, and deep learning features. Radiuma includes a full suite of machine learning tools, supporting the entire executable and graphical pipeline, from feature preprocessing and selection to the development of tools for classification, regression, and clustering. Each tool can be used independently or integrated into a complete workflow. Users can construct workflows that begin with loading multi-modality DICOM images, and continue through processing, diagnosis, and prognostic modeling. Executable and graphical workflows can be saved, modified, or extended later, and most importantly, shared with other researchers and users. This allows reproducible analysis using the same parameters and settings across datasets. Radiuma supports the principles of open science by providing a usable, reusable, reproducible, shareable, and executable workflow generator environment for the full pipeline of medical image analysis and machine learning.