Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Abstract

PURPOSE: To develop and compare an automated detection system for ischemic lesions in a neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO-H) from T2 weighted MRI (T2WI) to the currently used "gold standard" of manual segmentation. MATERIALS AND METHODS: Forty-three P10 BCAO-H rat pups and 8 controls underwent T2WI at 1 day and 28 days. A computational imaging method, Hierarchical Region Splitting (HRS), was developed to automatically and rapidly detect and quantify 3D lesion and normal appearing brain matter (NABM) volumes. RESULTS: HRS quantified lesion and NABM volumes within 15 s in comparison to 3 h for its manual counterpart, with a high correlation for injury (r(2) = 0. 95; P = 8.6 × 10(-7) ) and NABM (r(2) = 0. 92; P = 1.4 × 10(-22) ). Average lesion volumes for mild, moderate, and severe injuries were 3.85%, 28.85%, and 52.98% for HRS and 0.51%, 24.22%, and 48.74% for manual detection. Lesion volumes and locations were similar for both methods (sensitivity: 0.82, specificity: 0.86, and similarity: 1.47). CONCLUSION: HRS is an accurate, objective, and rapid method to quantify injury evolution in neonatal hypoxic ischemic injury models.