UC Santa Cruz

For comprehensive anatomical analysis of a mouse brain, accurate and efficient registration of the experimental brain samples to a reference atlas is necessary. Here, I introduce Bell Jar, an automated solution that can align and annotate tissue sections with anatomical structures from a reference atlas and detect fluorescent signals with cellular resolution (e.g., cell bodies or nuclei). Bell Jar utilizes machine learning-based non-linear image registration to achieve precise alignments, even with damaged sample tissues. While user input remains required for fine-tuning section matches, the platform streamlines the process, aiding rapid analyses in high-throughput neuroanatomy studies. As a standalone desktop application with a user-friendly interface, Bell Jar's performance surpasses traditional manual and existing automated methods and can improve the reproducibility and throughput of histological analyses.