University of Illinois Urbana-Champaign

Synthesizing a complete tomographic study with nodules from multiple radiograph views via deep generative models

Chen, Andrew

Content Files
CHEN-THESIS-2020.pdf

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https://hdl.handle.net/2142/108587

Description

Title
Synthesizing a complete tomographic study with nodules from multiple radiograph views via deep generative models
Author(s)
Chen, Andrew
Issue Date
2020-07-09
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-10-07T22:44:27Z
Keyword(s)
  • radiography
  • machine learning
  • generative modeling
  • synthesis
Abstract
Chest radiography encodes a 3D anatomy into a complex 2D representation. This projection creates distinctive challenges even for the most experienced radiologists as many critical findings are superimposed, often resulting in error or further imaging. In particular, it is difficult to visualize the volume and density of lung nodules in chest radiographs. A deep generative model, commonly used to synthesize realistic images, can be used to perform 2D to 3D translations. In this thesis, we propose a generative model, optimized using pixel-wise error, that can synthesize a complete tomographic study containing nodules from frontal and lateral chest x-ray radiographs. Additionally, the generated studies maintain the proper chest cavity structure.
Graduation Semester
2020-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/108587
Copyright and License Information
Copyright 2020 Andrew Chen

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Dissertations and Theses in Electrical and Computer Engineering