APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms

• URL: http://arxiv.org/abs/2606.30577v2
• Date: Tue, 30 Jun 2026 14:59:05 GMT
• Title: APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
• Abstract summary: APRIL-MedSeg is a YAML-driven modular framework for 2D medical image segmentation.<n>It provides a unified ecosystem that decomposes segmentation networks into reusable components.
• Score: 33.154957184521365
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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