Automatic Detection and Segmentation of Postoperative Cerebellar Damage Based on Normalization (2203.02042v1)

Abstract: Surgical resection is a common procedure in the treatment of pediatric posterior fossa tumors. However, surgical damage is often unavoidable and its association with postoperative complications is not well understood. A reliable localization and measure of cerebellar damage is fundamental to study the relationship between the damaged cerebellar regions and postoperative neurological outcomes. Existing cerebellum normalization methods are not reliable on postoperative scans, therefore current approaches to measure surgical damage rely on manual labelling. In this work, we develop a robust algorithm to automatically detect and measure cerebellum damage due to surgery using postoperative 3D T1 magnetic resonance imaging. In our proposed approach, normal brain tissues are first segmented using a Bayesian algorithm customized for postoperative scans. Next, the cerebellum is isolated by nonlinear registration of a whole brain template to the native space. The isolated cerebellum is then normalized into the spatially unbiased atlas (SUIT) space using anatomical information derived from the previous step. Finally, the damage is detected in the atlas space by comparing the normalized cerebellum and the SUIT template. We evaluated our damage detection tool on postoperative scans of 153 patients diagnosed with medulloblastoma based on inspection by human expects. We also designed a simulation to test the proposed approach without human intervention. Our results show that the proposed approach has superior performance on various scenarios.