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CyTran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation
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- Ristea, Nicolae-Catalin (author)
- Univ Bucharest, Romania; Univ Politehn Bucuresti, Romania; Mohamed bin Zayed Univ Artificial Intelligence, U Arab Emirates
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- Miron, Andreea-Iuliana (author)
- Coltea Hosp, Romania; Carol Davila Univ Med & Pharm, Romania
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- Savencu, Olivian (author)
- Coltea Hosp, Romania; Carol Davila Univ Med & Pharm, Romania
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- ELSEVIER, 2023
- 2023
- English.
- In: Neurocomputing. - : ELSEVIER. - 0925-2312 .- 1872-8286. ; 538
- Journal article (peer-reviewed)
Abstract
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- We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to noncontrast CT scans and the other way around. Solving this task has two important applications: (i) to automatically generate contrast CT scans for patients for whom injecting contrast substance is not an option, and (ii) to enhance the alignment between contrast and non-contrast CT by reducing the differences induced by the contrast substance before registration.Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our neural model can be trained on unpaired images, due to the integration of a multi-level cycleconsistency loss. Aside from the standard cycle-consistency loss applied at the image level, we propose to apply additional cycle-consistency losses between intermediate feature representations, which enforces the model to be cycle-consistent at multiple representations levels, leading to superior results. To deal with high-resolution images, we design a hybrid architecture based on convolutional and multi-head attention layers. In addition, we introduce a novel data set, Coltea-Lung-CT-100W, containing 100 3D triphasic lung CT scans (with a total of 37,290 images) collected from 100 female patients (there is one examination per patient). Each scan contains three phases (non-contrast, early portal venous, and late arterial), allowing us to perform experiments to compare our novel approach with state-of-the-art methods for image style transfer.Our empirical results show that CyTran outperforms all competing methods. Moreover, we show that CyTran can be employed as a preliminary step to improve a state-of-the-art medical image alignment method. We release our novel model and data set as open source at: https://github.com/ristea/cycletransformer.Our qualitative and subjective human evaluations reveal that CyTran is the only approach that does not introduce visual artifacts during the translation process. We believe this is a key advantage in our application domain, where medical images need to precisely represent the scanned body parts. (c) 2023 Elsevier B.V. All rights reserved.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Transformers; Generative adversarial transformers; Deep learning; Cycle-consistency; Image translation; Image registration; Computed tomography; Triphasic lung CT
Publication and Content Type
- ref (subject category)
- art (subject category)
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