APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
For researchers and developers in medical image segmentation, it offers a comprehensive, extensible platform to streamline algorithm development and comparison, though it is an incremental contribution as a toolbox rather than a novel method.
APRIL-MedSeg is a modular, YAML-driven framework for 2D medical image segmentation that integrates advanced paradigms like semi-supervised learning, domain adaptation, and text-guided segmentation. It provides a unified interface for datasets, augmentation, and deployment, enabling flexible and reproducible experimentation.
We present APRIL-MedSeg, a YAML-driven modular framework for 2D medical image segmentation. It provides a unified and extensible ecosystem that decomposes segmentation networks into reusable components. Also, the framework integrates a broad spectrum of advanced paradigms, including semi-supervised learning, domain adaptation, knowledge distillation, weakly supervised learning, and text-guided segmentation as well as foundation model support. A registry-based configuration system with inheritance enables flexible and reproducible experiment management, supporting seamless switching across models, datasets, and training strategies. In addition, the framework provides a unified interface for medical datasets, augmentation pipelines, deployment utilities and model ensembling. Overall, APRIL-MedSeg is designed as a general-purpose research and development platform that bridges algorithmic innovation and practical deployment, while also serving as a structured ecosystem for systematically organizing and reproducing advances in medical image segmentation. The code is available at https://github.com/juntaoJianggavin/APRIL-MedSeg under an Apache 2.0 license.