Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

Background And Objectives: In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data.

Methods: We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017.

Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images.

Not only the time-dependent varying of signal intensity (i.e. haematoma evolution) characteristics of the intracranial blood in computed tomography images, but also the fluctuating image quality, the distortions introduced after medical interventions, and the brain deformations and intensity profile variations due to underlying pathologies make the segmentation of intracranial blood a challenging task.