Autonomous navigation of a flexible surgical robot in the lungs
Abstract/Contents
- Abstract
- 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Sganga, Jake Anthony |
|---|---|
| Degree supervisor | Camarillo, David |
| Thesis advisor | Camarillo, David |
| Thesis advisor | Okamura, Allison |
| Thesis advisor | Schwager, Mac |
| Degree committee member | Okamura, Allison |
| Degree committee member | Schwager, Mac |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jake Sganga. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Jake Anthony Sganga
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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