Deep Learning-Based Automated MRI Image Segmentation

by Zhao, Tianyun

Type Thesis
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This thesis's work focuses on the clinical applications of U-Net on MRI image segmentation, with various dimensions and depths based on the clinical question. Chapter 1 is a brief introduction to magnetic resonance imaging. Chapter 2 introduces convolutional neural network and U-Net. Chapter 3 focuses on 2D U-Net based lumbar segmentation used for a radiomic pipeline to predict osteoporosis; reliability was assessed by comparing to manual segmentation. Chapter 4 includes a 3D U-Net developed to…

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Summary

This thesis's work focuses on the clinical applications of U-Net on MRI image segmentation, with various dimensions and depths based on the clinical question. Chapter 1 is a brief introduction to magnetic resonance imaging. Chapter 2 introduces convolutional neural network and U-Net. Chapter 3 focuses on 2D U-Net based lumbar segmentation used for a radiomic pipeline to predict osteoporosis; reliability was assessed by comparing to manual segmentation. Chapter 4 includes a 3D U-Net developed to help with automated breast density measurement. The segmentation reliability was assessed by studying test-retest reliability of the breast density. Chapter 5 contains a 2.5D U-Net used to segment fat pads for future radiomic study attempting to predict osteoarthritis progression.

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Edition Statements

  • Publication Date:2020

Thesis Information

  • Degree:M.S.
  • University:State University of New York at Stony Brook
  • Advisor/Supervisor:Huang, Chuan
  • Pages:54

Publication Statements

  • Language:eng
  • Publication Date:2020

Edition Identifiers

  • proquest:2478808272

Subjects

artificial intelligence biomedical engineering eng Medical imaging PDF

Writers

Zhao, Tianyun Huang, Chuan

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