2026-08-12 –, Room 4
Radiomic features extracted from medical images are fundamental for computer-aided diagnosis and treatment planning.
Radiomics.jl is a new, pure-Julia open-source library for high-performance extraction of quantitative imaging biomarkers.
Developed across multiple international institutions, it provides an efficient and scalable workflow by leveraging Julia’s speed.
The library ensures seamless integration with machine learning pipelines for advanced clinical research and precision medicine.
Introduction
Radiomics has emerged as a fundamental approach in precision medicine, enabling the extraction of high-throughput quantitative features from medical images for computer-aided diagnosis and personalized treatment planning [1].
In this work, Radiomics.jl [2], a new and open-source Julia library designed to extract radiomic features, is presented.
Materials and Methods
Radiomics.jl implements a complete set of radiomic features (106 in total) extraction capabilities, encompassing first-order and 2D/3D shape-based features, as well as sophisticated texture descriptors. These advanced texture features include the Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Dependence Matrix (GLDM), Gray-Level Run-Length Matrix (GLRLM), Gray-Level Size-Zone Matrix (GLSZM), and Neighborhood Gray-Tone Difference Matrix (NGTDM).
The tool is designed for convenience and user-friendliness, and it takes advantage of a multi-threading workflow, which allows efficient feature extraction from multiple tissues or organs concurrently.
Results
A Computed Tomography (CT) scan of a patient with a lung tumor [3,4] was selected for benchmarking. The CT volume dimensions were 500 x 500 x 319 voxels, with the tumor segmentation comprising 38226 voxels (38.226 cm3). Excluding the initial JIT compilation overhead, which accounted for 12.47 s (single-thread) and 5.60 s (multi-thread), the mean execution time over 14 repetitions was 4.07 s (SD=0.10 s) for single-thread and 3.00 s (SD=0.29 s) for multi-thread. Multi-threading yielded a 25% reduction in computational time (Wilcoxon rank-sum test revealed statistically significant difference between the two distributions).
Discussion and Conclusions
Radiomics.jl offers a high-performance, efficient, and user-friendly solution for quantitative medical image analysis entirely within the Julia ecosystem.
Designed for fast and user-friendly feature extraction from medical images, its goal is to support and advance the field of personalized medicine.
References
[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC4734157/
[2] https://github.com/pzaffino/Radiomics.jl
[3] Aerts HJWL, et al. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006
[4] Aerts HJWL, et al. NSCLC-Radiomics [Data set]. TCIA, 2014. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI
My name is Aldo Giuliani. I have a master’s degree in Biomedical Engineering and I am a PhD student in Artificial Intelligence and Biomedical Engineering at the “Magna Graecia” University of Catanzaro, Italy.