HISRON: AI-Driven GPU-Accelerated Framework for Scalable High-Resolution Neuroimaging Analysis.

Area of Science:

• Neuroscience
• Computer Vision
• Biomedical Imaging

Background:

• Ultra-high-resolution cellular neuroimaging generates massive datasets, posing significant computational challenges.
• Existing methods struggle with the scale and complexity of terabyte-sized neuroimaging data.
• Efficient analysis is crucial for diagnosing neurological disorders and advancing brain research.

Purpose of the Study:

• To present HISRON (High-Resolution Scalable Neuroimaging), a GPU-accelerated framework for scalable multidimensional neuroimaging analysis.
• To overcome computational bottlenecks in processing and analyzing large-scale, high-resolution neuroimaging data.
• To enhance the integration of AI-driven pipelines for segmentation and classification in neuroscience.

Main Methods:

• Developed a GPU-accelerated framework (HISRON) utilizing NVIDIA CUDA and CuPy.
• Integrated unsupervised learning for adaptive feature extraction and benchmarked anisotropic diffusion for noise reduction.
• Employed promptable segmentation models with zero-shot generalization and dynamic overlap-aware tiling for parallelization.

Main Results:

• Achieved 10x faster noise reduction and detected 200,000 neuron centroids in 30 seconds.
• Demonstrated a 40% improvement in overall pipeline efficiency.
• Enabled real-time processing of complex structures by maximizing parallelization while preserving spatial context.

Conclusions:

• HISRON significantly accelerates the analysis of biomedical imaging data, supporting translational neuroscience and healthcare innovations.
• The framework's scalability and adaptability address critical challenges in clinical neuroimaging, aiding diagnosis and treatment planning.
• HISRON democratizes advanced imaging analytics, empowering research and clinical settings with efficient, AI-driven solutions for neurological studies.