Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

TL;DR

This paper introduces a GPU-accelerated 3D active contour method for efficient and automated segmentation of cells in large microscopy images, significantly improving speed and accuracy over existing techniques.

Contribution

It presents a novel parallel formulation for 3D active contours optimized for GPU hardware, enabling faster segmentation of large-scale microscopy images.

Findings

Outperforms existing methods on large 3D brain images

Achieves faster convergence with Monte-Carlo sampling

Provides scalable segmentation for large 2D and 3D images

Abstract

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images.