OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation

2026-07-31

AI Summary

This work addresses the challenge faced by resource-constrained medical institutions—limited computational capacity and insufficient expertise—that hinders the deployment of deep learning for bone tumor segmentation. To overcome this barrier, the authors propose a modular cloud-edge collaborative framework that encapsulates the entire pipeline, from data creation and preprocessing to model training and inference, into user-friendly interfaces through a human-in-the-loop architecture. By leveraging remote GPU resources, dynamic GPU scheduling, and an intuitive interactive interface within a cloud-edge computing paradigm, the system enables low-barrier AI deployment. Its efficacy is demonstrated in a real-world clinical setting in Mexico, where it successfully performed large-scale bone tumor segmentation, confirming the feasibility of deploying effective AI-driven medical solutions without requiring complex local infrastructure.

Abstract

Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framework that democratizes access to DL tools in clinical practice. Osteo- CAD delivers end-to-end DL capabilities-from dataset creation and preprocessing to model training and inference-through an integrated and user-friendly interface. To mitigate local hardware constraints, the framework securely connects to remote GPU infrastructures. We validate OsteoCAD's feasibility through a real-world case study in Mexico focused on large bone tumor segmentation. The results demonstrate the framework's ability to enable DL-powered eHealth solutions without demanding ad- vanced technical expertise or complex local configurations.

Problem

• medical image analysis
• bone tumor segmentation
• computational resources
• technical expertise
• deep learning adoption

Innovation

• human-in-the-loop
• cloud-edge computing
• bone tumor segmentation
• deep learning democratization
• eHealth framework