Authors:
Arthur Longuefosse
1
;
Baudouin Denis De Senneville
2
;
Gaël Dournes
3
;
Ilyes Benlala
3
;
François Laurent
3
;
Pascal Desbarats
1
and
Fabien Baldacci
1
Affiliations:
1
LaBRI, Université de Bordeaux, Talence, France
;
2
Institut de Mathématiques de Bordeaux, Université de Bordeaux, Talence, France
;
3
Service d’Imagerie Médicale Radiologie Diagnostique et Thérapeutique, CHU de Bordeaux, France
Keyword(s):
Generative Adversarial Networks, CT Synthesis, Lung.
Abstract:
In medical imaging, MR-to-CT synthesis has been extensively studied. The primary motivation is to benefit
from the quality of the CT signal, i.e. excellent spatial resolution, high contrast, and sharpness, while avoiding
patient exposure to CT ionizing radiation, by relying on the safe and non-invasive nature of MRI. Recent
studies have successfully used deep learning methods for cross-modality synthesis, notably with the use of
conditional Generative Adversarial Networks (cGAN), due to their ability to create realistic images in a target
domain from an input in a source domain. In this study, we examine in detail the different steps required
for cross-modality translation using GANs applied to MR-to-CT lung synthesis, from data representation and
pre-processing to the type of method and loss function selection. The different alternatives for each step were
evaluated using a quantitative comparison of intensities inside the lungs, as well as bronchial segmentations
between s
ynthetic and ground truth CTs. Finally, a general guideline for cross-modality medical synthesis is
proposed, bringing together best practices from generation to evaluation.
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