Domain-Invariant Brainstem Nuclei Segmentation and Signal Quantification

Abstract

AO_SCPLOWBSTRACTC_SCPLOWBrainstem nuclei are hard to distinguish due to very few distinctive features which makes detecting them with high accuracy extremely difficult. We introduce StARQ that builds on SeBRe, a deep learning-based framework to segment regions of interest. StARQ provides new functionalities for automated segmentation of brainstem nuclei at high granularity, and quantification of underlying neural features such as axonal tracings, and synaptic punctae. StARQ will serve as a toolbox for generalized brainstem analysis, enabling reliable high-throughput computational analysis with open-source models.