Neonatal hypoxic-ischemic brain injury (HII) and arterial ischemic stroke (AIS) result in irreversibly injured (core) and salvageable (penumbral) tissue regions. Identification and reliable quantification of salvageable tissue is pivotal to any effective and safe intervention. Magnetic resonance imaging (MRI) is the current standard to distinguish core from penumbra using diffusion-perfusion mismatch (DPM). However, subtle MR signal variations between core-penumbral regions make their visual delineation difficult. We hypothesized that computational analysis of MRI data provides a more accurate assessment of core and penumbral tissue evolution in HII/AIS. We used two neonatal rat-pup models of HII/AIS (unilateral and global hypoxic-ischemia) and clinical data sets from neonates with AIS to test our noninvasive, automated computational approach, Hierarchical Region Splitting (HRS), to detect and quantify ischemic core-penumbra using only a single MRI modality (T2- or diffusion-weighted imaging, T2WI/DWI). We also validated our approach by comparing core-penumbral images (from HRS) to DPM with immunohistochemical validation of HII tissues. Our translational and clinical data results showed that HRS could accurately and reliably distinguish the ischemic core from penumbra and their spatiotemporal evolution, which may aid in the vetting and execution of effective therapeutic interventions as well as patient selection.