Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119697 |
| _version_ | 1826204255943393280 |
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| author | Xue, Elise Yuan |
| author2 | Brian W. Anthony. |
| author_facet | Brian W. Anthony. Xue, Elise Yuan |
| author_sort | Xue, Elise Yuan |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T12:51:25Z |
| format | Thesis |
| id | mit-1721.1/119697 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:51:25Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1196972019-04-12T17:45:39Z Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data Sensorless ultrasound probe six degree of freedom pose estimation through the use of CNNs on image data Xue, Elise Yuan Brian W. Anthony. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 55-57). Ultrasound probe pose estimation has many applications in medical practice and research. Currently, ultrasound probe pose estimation with respect to the human body requires the use of sensors attached to the ultrasound probe, and may get computationally costly. We explore the use of Convolutional Neural Networks (CNNs) to provide sensorless pose estimation. The Ultrasound CNN model proposed in this paper learns to regress the six degree of freedom (6-DoF) camera pose from a single ultrasound image in an end-to-end manner. Ultrasound images are easier to obtain than other forms of medical imaging, but suffer from poor quality, which will be a challenge for the Ultrasound CNN model. The most promising model from our experiments is a 23 layer deep CNN based off of GoogLeNet. In previous literature, CNNs have demonstrated that they can be used to solve complicated out of image plane regression problems. We show how the proposed method can regress the 6DoF pose within a certain degree of accuracy. by Elise Yuan Xue. M. Eng. 2018-12-18T19:46:13Z 2018-12-18T19:46:13Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119697 1078150297 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 57 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Xue, Elise Yuan Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title | Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title_full | Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title_fullStr | Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title_full_unstemmed | Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title_short | Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data |
| title_sort | sensorless ultrasound probe 6dof pose estimation through the use of cnns on image data |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119697 |
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