Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery by Loris Fichera.

This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons’ perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative...

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Bibliographic Details
Main Author: Fichera, Loris. (Author, http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Springer Theses, Recognizing Outstanding Ph.D. Research,
Springer eBook Collection.
Subjects:
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
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505 0 |a Introduction -- Background: Laser Technology and Applications to Clinical Surgery -- Cognitive Supervision for Transoral Laser Microsurgery -- Learning the Temperature Dynamics During Thermal Laser Ablation -- Modeling the Laser Ablation Process -- Realization of a Cognitive Supervisory System for Laser Microsurgery -- Conclusions and Future Research Directions. 
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520 |a This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons’ perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures. 
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