🤖 AI Summary
Large-scale high-resolution 3D volumetric data (e.g., X-ray tomography) continue to grow in size, posing challenges for existing segmentation and interactive analysis tools—including computational inefficiency, memory bottlenecks, and insufficient scalability for collaborative workflows. To address these, we propose a CUDA-based tiled execution architecture with strict memory management, integrated into the Annotat3D system via the Harpia library, enabling stable processing of ultra-large volumes under constrained single-GPU memory budgets. Our approach combines GPU-accelerated filtering, dynamic tiling scheduling, and human-in-the-loop interaction strategies to form an efficient 3D processing pipeline. Experimental evaluation demonstrates significant improvements over cuCIM and scikit-image in processing speed, memory efficiency, and strong scaling across multi-node HPC environments. The framework supports real-time, remote collaborative scientific imaging analysis.
📝 Abstract
High-resolution volumetric imaging techniques, such as X-ray tomography and advanced microscopy, generate increasingly large datasets that challenge existing tools for efficient processing, segmentation, and interactive exploration. This work introduces new capabilities to Annotat3D through Harpia, a new CUDA-based processing library designed to support scalable, interactive segmentation workflows for large 3D datasets in high-performance computing (HPC) and remote-access environments. Harpia features strict memory control, native chunked execution, and a suite of GPU-accelerated filtering, annotation, and quantification tools, enabling reliable operation on datasets exceeding single-GPU memory capacity. Experimental results demonstrate significant improvements in processing speed, memory efficiency, and scalability compared to widely used frameworks such as NVIDIA cuCIM and scikit-image. The system's interactive, human-in-the-loop interface, combined with efficient GPU resource management, makes it particularly suitable for collaborative scientific imaging workflows in shared HPC infrastructures.