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Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography
Antonios Thanellas1, Heikki Peura2, Jenni Wennervirta2
1Department of Information Management, Helsinki University Hospital, Helsinki, Finland.
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. In addition to describing various challenges with blood segmentation, this chapter also reviews the following: (1) the general concept of segmentation-explaining why a proper segmentation is a critical step when creating machine learning algorithms for image detection purposes, (2) the different segmentation types and how different medical conditions and technical issues can further complicate this task, (3) how to choose a proper software to facilitate the segmentation task, and (4) useful tips that may be applied before launching a similar segmentation project.
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. In addition to describing various challenges with blood segmentation, this chapter also reviews the following: (1) the general concept of segmentation-explaining why a proper segmentation is a critical step when creating machine learning algorithms for image detection purposes, (2) the different segmentation types and how different medical conditions and technical issues can further complicate this task, (3) how to choose a proper software to facilitate the segmentation task, and (4) useful tips that may be applied before launching a similar segmentation project.
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