P-ODA: A Fast and Robust Parallel Ommatidia Detection Algorithm for High-Throughput Insect Ommatidia Detection

The automatic detection and analysis of ommatidia, the fundamental visual units of insect compound eyes, is vital for advancing biological research and understanding insect vision. Compound eyes, with their multi-lens structure, provide a broad field of view and high visual resolution. Researchers typically obtain ommatidia structures through CT imaging of insect compound eyes. However, processing these high-resolution 3D images requires significant computational resources due to the complexity and scale of the data. Existing methods struggle with inefficiency and poor robustness, particularly when dealing with large and complex datasets. To overcome these challenges, we introduce a groundbreaking framework, P-ODA, for fast and robust ommatidia detection by integrating parallel computing into the CT image analysis insect compound eyes. By harnessing the power of both Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) and multicore Central Processing Units (CPUs), we present a novel approach to enhance the Ommatidia Detection Algorithm (ODA). Our method efficiently processes large-scale datasets and demonstrates unprecedented scalability. We offer two independent implementations: one utilizing CUDA for GPU acceleration, and another leveraging Python's multicore CPU capabilities. Experimental results show that P-ODA dramatically improves execution time and scalability over traditional ODA methods. Moreover, the system is designed for ease of use with a Linux graphical interface. This innovative framework sets the stage for the next generation of biological dataset processing and hybrid parallel computing systems.