. Purpose To evaluate nn-Unet-based segmentation models for automated delineation of medulloblastoma (MB) tumors on multi-institutional MRI scans. Materials and Methods This retrospective study included 78 pediatric patients (52 male, 26 female), with ages ranging from 2-18 years, with MB tumors from three different sites (28 from Hospital A, 18 from Hospital B, 32 from Hospital C), who had data from three clinical MRI protocols (gadolinium-enhanced T1-weighted, T2-weighted, FLAIR) available. The scans were retrospectively collected from the year 2000 until May 2019. Reference standard annotations of the tumor habitat, including enhancing tumor, edema, and cystic core + nonenhancing tumor subcompartments, were performed by two experienced neuroradiologists. Preprocessing included registration to age-appropriate atlases, skull stripping, bias correction, and intensity matching. The two models were trained as follows: (1) transfer learning nn-Unet model was pretrained on an adult glioma cohort (

nnU-Net–based Segmentation of Tumor Subcompartments in Pediatric Medulloblastoma Using Multiparametric MRI: A Multi-institutional Study

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