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

Ultra-high-resolution imaging, particularly cellular neuroimaging, poses challenges from terabyte-scale data and computational complexity. We present HISRON (High-Resolution Scalable Neuroimaging) a GPU-accelerated framework enabling scalable multidimensional analysis, combining unsupervised learning for adaptive feature extraction, benchmarked anisotropic diffusion for noise reduction, and promptable segmentation models with a zero-shot generalization feature. Dynamic overlap-aware tiling maximizes parallelization while preserving spatial context, enabling real-time processing of complex structures. Built on NVIDIA CUDA and CuPy, the framework achieves transformative efficiency: 10x faster noise reduction and detection of 200,000 neuron centroids in 30 seconds (40% pipeline improvement). This advances integration with AI-driven segmentation/classification pipelines, overcoming bottlenecks in high-dimensional computer vision. By emphasizing scalability, our method accelerates analysis of biomedical imaging data, directly supporting translational healthcare innovations in neuroscience. The tool's adaptability underscores its potential for clinical research, enhancing precision in neuroanatomical studies and fostering discoveries in brain function and pathology.Clinical relevance-The proposed framework directly addresses critical challenges in modern clinical neuroimaging, where the analysis of high-resolution data is essential for diagnosing and monitoring neurological disorders such as Alzheimer's, Parkinson's diseases, and epilepsy. By enabling real-time processing of terabyte-scale datasets, this technology reduces delays in image interpretation, facilitating faster decision-making in time-sensitive scenarios, such as intraoperative imaging during neurosurgery or stroke assessment. The zero-shot segmentation model's adaptability ensures robust performance across heterogeneous imaging protocols, which is vital for multicenter clinical studies and personalized treatment planning. Additionally, the framework's efficiency in detecting neuron populations at scale supports large-scale neuroanatomical studies, enhancing our understanding of brain connectivity abnormalities in psychiatric and neurodegenerative conditions. By lowering computational barriers, this tool democratizes access to advanced imaging analytics, empowering clinics with limited resources to adopt precision medicine approaches. These advancements align with the growing demand for AI-driven scalable solutions to improve diagnostic accuracy, accelerate therapeutic discovery, and optimize patient outcomes in neurology and neurorehabilitation.