Abstract
Investigation of the 3D neuron morphology plays a critical part in understanding functions and activities in brain circuits, towards discovering how the brain develops in health and how brain function can be modified to improve human well-being. One of the major procedures in rebuilding connections and structures of neural circuits is to reconstruct the neuron morphology from 3D optical microscopy images. This has traditionally been done manually and is extremely time consuming. Many research studies have thus been conducted for automatic neuronal structure reconstruction with the aim of achieving high-quality reconstruction under poor image quality. In this chapter, we present several case studies of our most recent AI method development, which consists of novel 3D deep learning models for neural structure segmentation and intelligent back-tracking approaches for tracing. These methods are generally effective for a large variety of species and provide the state-of-the-art performance in single neuron reconstruction. Such development will enable large-scale data-driven investigations in neuroscience and enhance our fundamental understanding of the human brain.
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Wang, H. et al. (2022). AI-Enhanced 3D Biomedical Data Analytics for Neuronal Structure Reconstruction. In: Chen, F., Zhou, J. (eds) Humanity Driven AI. Springer, Cham. https://doi.org/10.1007/978-3-030-72188-6_7
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