Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation

TL;DR

Harpia is a CUDA-based library that significantly improves large-scale 3D data segmentation by enabling scalable, interactive, and memory-efficient processing in high-performance computing environments.

Contribution

This work introduces Harpia, a novel CUDA-accelerated library that enhances Annotat3D with scalable, GPU-accelerated segmentation tools for large volumetric datasets.

Findings

Harpia outperforms existing frameworks in speed and memory efficiency.

Supports datasets exceeding single-GPU memory capacity.

Enables interactive segmentation workflows in HPC environments.

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.