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Autonomous Navigation of a Flexible Surgical Robot in the Lungs.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Autonomous Navigation of a Flexible Surgical Robot in the Lungs./
作者:	Sganga, Jake.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
標題:	Bioengineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28113531
ISBN:	9798698529125

Autonomous Navigation of a Flexible Surgical Robot in the Lungs.
Sganga, Jake.

Autonomous Navigation of a Flexible Surgical Robot in the Lungs. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 135 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2019.

This item must not be sold to any third party vendors.

Lung cancer is the leading cause of cancer-related death worldwide, and early diagnosis is critical to improving patient outcomes. To diagnose cancer, a highly trained pulmonologist must navigate a flexible bronchoscope deep into the branched structure of the lung for biopsy. The biopsy fails to sample the target tissue in 26-33% of cases largely because of poor registration with the preoperative CT map. If the localization were sufficiently precise, a closed-loop control system could drive the bronchoscope without human intervention. Automation may de-skill standard bronchoscopies, potentially reducing the cost of the procedure with a single pulmonologist monitoring multiple simultaneous procedures. We sought to enable autonomous navigation of the airways by advancing the intraoperative registration methods and the control of the flexible surgical robots. To improve intraoperative registration, we develop three deep learning approaches to localize the bronchoscope in the preoperative CT map based on the bronchoscopic video in real-time, called OffsetNet, AirwayNet, and BifurcationNet. The networks are trained entirely on simulated images derived from the patient-specific CT. The networks are evaluated on recorded bronchoscopy videos in a phantom lung and recorded videos in human cadaver lungs. AirwayNet outperforms other deep learning localization algorithms with an area under the precision-recall curve of 0.97 in the phantom lung, and areas ranging from 0.82 to 0.997 in the human cadaver lungs. To improve the control of flexible surgical robots, we developed an state estimation algorithm that adapted to the unknown contacts of the robot with the lung's environment. Using AirwayNet and the motion controller, we demonstrate autonomous driving in the phantom lung based on video feedback alone. The robot reaches four targets in the left and right lungs in 95% of the trials.

ISBN: 9798698529125Subjects--Topical Terms:

657580
Bioengineering.
Subjects--Index Terms:

Autonomous navigation

Autonomous Navigation of a Flexible Surgical Robot in the Lungs.
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