OmniMedSeg: A Large-Scale Standardized Benchmark for Interactive Medical Image Segmentation

As the field grows, the number of publicly annotated datasets has increased substantially. However, due to the sensitivity of medical data, many datasets are restricted by licenses, limited to specific challenges, or require complex access procedures.

Moreover, medical imaging data is highly heterogeneous, existing in various formats that are often incompatible and difficult to use directly for model training or evaluation. In this paper, we address these issues by presenting \textit{OmniMedSeg} - a large-scale, standardized benchmark for medical image segmentation that unifies 156 openly licensed datasets spanning nine imaging modalities. We introduce a modular three-layer framework (Download, Conversion, and Data layers) that automates data retrieval, standardizes formats to PNG for 2D and NIfTI for 3D data, and preserves all original metadata with full traceability.