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

Antonios Thanellas, Heikki Peura, Jenni Wennervirta, Miikka Korja

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKirjan luku tai artikkeliTieteellinenvertaisarvioitu

Abstrakti

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.

Alkuperäiskielienglanti
OtsikkoMachine Learning in Clinical Neuroscience
ToimittajatVictor E. Staartjes, Luca Regli, Carlo Serra
Sivumäärä7
JulkaisupaikkaCham
KustantajaSpringer Science and Business Media Deutschland GmbH
Julkaisupäivä2022
Painos1st ed.
Sivut153-159
ISBN (painettu)978-3-030-85291-7
ISBN (elektroninen)978-3-030-85292-4
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA3 Kirjan tai muun kokoomateoksen osa

Julkaisusarja

NimiActa Neurochirurgica, Supplementum
Vuosikerta134
ISSN (painettu)0065-1419
ISSN (elektroninen)2197-8395

Lisätietoja

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Tieteenalat

  • 3112 Neurotieteet
  • 3124 Neurologia ja psykiatria

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