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Proceedings Paper

Medical (CT) image generation with style
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Paper Abstract

We propose the use of a conditional generative adversarial network (cGAN) to generate anatomically accurate full-sized CT images. Our approach is motivated by the recently discovered concept of style transfer and proposes to mix style and content of two separate CT images for generating a new image. We argue that by using these losses in a style transfer based architecture along with a cGAN, we can increase the size of clinically accurate, annotated datasets by multiple folds. Our framework can generate full-sized images with novel anatomy at spatial high resolution for all organs and only requires limited annotated input data of a few patients. The expanded datasets our framework generates can then be utilized within the many deep learning architectures designed for various processing tasks in medical imaging.

Paper Details

Date Published: 28 May 2019
PDF: 4 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107234 (28 May 2019); doi: 10.1117/12.2534903
Show Author Affiliations
Arjun Krishna, Stony Brook Univ. (United States)
Klaus Mueller, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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