We have developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis. We developed atlas-to-brain and brain-to-atlas approaches to make the software flexible, easy to use and robust. We offer an easy-to-use GUI, as well as a powerful command line interface (CLI) allowing you to integrate the toolbox with your own neural imaging workflow. We also extend our pipeline to make use of functional sensory maps and spontaneous cortical activity motifs. We developed novel animal-specific motif-based functional maps that represent cortical consensus patterns of regional activation that can be used for brain registration and segmentation. We provided six end-to-end automated pipelines to allow users to quickly output results from input images. We provided Code Ocean capsules to demonstrate the operation of all these automated MesoNet pipelines at 10.24433/CO.1919930.v1, and 10.24433/CO.4985659.v1. Our GitHub repository - through which you can install and find instructions on how to use MesoNet - can be found here.