While the exact prevalence of thyroid nodules in the wider population is not known, studies have shown them to be prevalent in between 2% and 65% of the the human population. The vast majority of these tumors are benign. However, the rate of thyroid cancer has at least doubled since 1980 while a corresponding increase in mortality has not occurred, leading researchers to conclude the rise is a measure of how many cancerous nodules are being found. Physicians have turned to ultrasonography as a relatively low-cost method of imaging thyroid nodules in a non-invasive way. These exams, when conducted by physicians and trained ultrasound technicians, are prone to human error which can cause inconsistent results between exams. This thesis proposes a novel robotic system for assisting physicians in conducting thyroid ultrasound exams. The proposed system uses a seven degree of freedom robotic manipulator mounted with a wireless ultrasound scanner to capture ultrasound images at known locations and a two-stage image segmentation framework to track the region of interest identified within the ultrasound image. The first stage is a real-time, non-machine learning image segmentation algorithm, initialized by the physician, that tracks the region of interest throughout the examination assisted by a force, scanner orientation, and position control framework. The second stage of the framework is a non-real time, non-machine learning image segmentation algorithm which, combined with the positional data from the robot, generates an approximation of the region of interest as a volume and displays a 3D representation of this volume. Testing completed on a training phantom demonstrated the ability of the system to track the patient profile accurately and in real-time. From the experimental data, a volume was repeatedly generated and visualized properly.

Creator

• Mart, Benjamin

Contributeurs

• Zhang, Haichong
• Zheng, Yihao
• Aloi, Vincent

Degree

• MS

Unit

• Robotics Engineering

Publisher

• Worcester Polytechnic Institute

Identifier

• etd-121520

Mot-clé

• Volume Reconstruction
• Orientation Optomisation
• Image Segmentation
• Ultrasound Imaging
• Thyroid Examination
• Robotic Ultrasound

Advisor

• Zhang, Haichong

Orcid

• https://orcid.org/0009-0006-8254-5830

Committee

• Zheng, Yihao
• Aloi, Vincent

Defense date

• 2024-03-18

Year

• 2024

Date created

• 2024-04-24

Resource type

• Thesis

Source

• etd-121520

Rights statement

• In Copyright