Case Study: Hannover Medical School achieves AI-driven lung segmentation, cutting evaluation from days to minutes and processing 5× more data with NetApp

Leading doctors are building data-driven diagnostics on NetApp to help improve patients’ lives

Hannover Medical School (MHH) is a leading German university hospital focused on medical education, research, and patient care, with major programs in radiology, transplantation, and biomedical engineering. Clinicians faced a bottleneck in COPD imaging: manual lung segmentation was time-consuming, costly, and required highly skilled doctors, often taking a full day per case and struggling to handle increasingly large image datasets.

By deploying NetApp’s data infrastructure (with partner Anders & Rodewyk) to support deep learning and AI workflows, MHH automated lung segmentation and matched storage performance to compute power. The solution improved image quality, accelerated the data pipeline, enabled processing of five times more data concurrently, and reduced evaluation times from hours to minutes, speeding diagnostics and easing clinician workload.

Hannover Medical School

Hinrich Winther

Resident