AI-Driven Scan-to-Discovery in 20 Minutes: Automated Segmentation of Synchrotron Micro-CT at Scale

Synchrotron beamlines generate high-resolution volumetric datasets that reveal detailed sample microstructure, but converting raw scans into scientific insight remains computationally demanding. We present an automated AI-driven high-performance computing (HPC) pipeline for near real-time segmentation and analysis of micro-CT scans collected at Beamline 8.3.2 at the Advanced Light Source (ALS). After acquisition, data are automatically transferred to HPC systems at the National Energy Research Scientific Computing Center (NERSC) and the Argonne Leadership Computing Facility (ALCF) for distributed tomographic reconstruction, followed by large-scale segmentation using two fine-tuned state-of-the-art foundation models, Segment Anything Model 3 (SAM3) and a Self-Distillation with No Labels (DINO)-based segmentation model, DINOv3-Seg. Their results are aggregated to produce final segmentation masks. Orchestrated with Prefect, it achieves a scan-to-discovery turnaround time of approximately 20 minutes. The pipeline generalizes across diverse micro-CT experiments; here, we applied it to examine xylem vessels in grapevine petiole datasets, revealing structural changes for in situ plant physiology studies.