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Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring

Wirkert, Sebastian Josef

Abstract (englisch):

Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physician’s perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by “imitating” the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the technique’s full potential.

The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible.
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Volltext §
DOI: 10.5445/IR/1000086188
Veröffentlicht am 08.10.2018
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Publikationsjahr 2018
Sprache Englisch
Identifikator urn:nbn:de:swb:90-861886
KITopen-ID: 1000086188
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XII, 144 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdatum 18.01.2018
Schlagwörter laparoscopy, multi-spectral, imaging, machine learning, deep learning, ischemia, perfusion, monte carlo, live, vide rate, surgery
Referent/Betreuer Dillmann, R.
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