Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data
Name
1078150297-MIT.pdf
Description
Full printable version
Size
4.57 MB
Format
Adobe PDF
Checksum (MD5)
5e1479de54fd7234283ed7456cc47c32
Author(s)
Xue, Elise Yuan
Advisor(s)
Brian W. Anthony.
Alternative Title
Sensorless ultrasound probe six degree of freedom pose estimation through the use of CNNs on image data
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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