Automated Image Analysis of Cranial Non-Contrast CT

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A. Patel

Promotor: B. van Ginneken and M. Prokop
Copromotor: R. Manniesing
Graduation year: 2023
Radboud University, Nijmegen

Abstract

Stroke and intracranial hemorrhage (a brain bleed) are serious medical conditions that require fast and accurate diagnosis to aid clinicians in making treatment decisions. Computed tomography (CT) is a widely available and fast imaging technique used for diagnosis, but it relies on interpretation by a clinician. To aid in the diagnosis of stroke and intracranial hemorrhage, artificial intelligence algorithms for computer-aided diagnosis (CAD) have been developed to automatically analyze CT images. This research presents different methods that demonstrate accurate segmentations of large cerebral anatomy and the ability to quantify and segment intracerebral hemorrhages for further analysis. Whole 3D non-contrast CT images were also analyzed automatically to identify the presence of intracranial hemorrhage with high sensitivity. With further development, CAD systems will be able to assist physicians in diagnosing and predicting outcomes of stroke and intracranial hemorrhage in clinical settings.