An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

Cerebral organoids recapitulate the structure and function of the developing human brain in vitro,
offering a large potential for personalized therapeutic strategies. The enormous growth of this
research area over the past decade with its capability for clinical translation makes a non‑invasive,
automated analysis pipeline of organoids highly desirable. This work presents a novel non‑invasive
approach to monitor and analyze cerebral organoids over time using high‑field magnetic resonance
imaging and state‑of‑the‑art tools for automated image analysis. Three specific objectives are
addressed, (I) organoid segmentation to investigate organoid development over time, (II) global
cysticity classification and (III) local cyst segmentation for organoid quality assessment. We show
that organoid growth can be monitored reliably over time and cystic and non‑cystic organoids can be
separated with high accuracy, with on par or better performance compared to state‑of‑the‑art tools
applied to brightfield imaging. Local cyst segmentation is feasible but could be further improved in the
future. Overall, these results highlight the potential of the pipeline for clinical application to larger‑
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scale comparative organoid analysis.