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Unpaired PET/CT image synthesis of liver region using CycleGAN

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Abstract

Cross-modality synthesis represent nowadays a promising application in medical image processing to manage the problem of paired data scarcity. In this work we designed and trained a CycleGAN model to generate PET/CT data from 2D slices collected from the liver body region of twelve patients. The results obtained from the six test patients show how our model can outperform baseline CycleGAN framework and effectively be used for synthesizing artificial images to be used for data augmentation or dataset completion.
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Dates and versions

inserm-03500317 , version 1 (22-12-2021)

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Gianmarco Santini, Constance Fourcade, Noémie Moreau, Caroline Rousseau, Ludovic Ferrer, et al.. Unpaired PET/CT image synthesis of liver region using CycleGAN. 16th International Symposium on Medical Information Processing and Analysis, Oct 2020, Lima, Peru. pp.6, ⟨10.1117/12.2576095⟩. ⟨inserm-03500317⟩
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