Medical (CT) image generation with style

Arjun Krishna; Klaus Mueller

doi:10.1117/12.2534903

28 May 2019 Medical (CT) image generation with style

Arjun Krishna, Klaus Mueller

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107234 (2019) https://doi.org/10.1117/12.2534903
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

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.

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Arjun Krishna and Klaus Mueller "Medical (CT) image generation with style", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107234 (28 May 2019); https://doi.org/10.1117/12.2534903

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KEYWORDS

Computed tomography

Medical imaging

Image processing

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