Neural Contrast Enhancement of CT Image
| Autor: | Jung Hoon Kim, Kyungmoon Lee, Dongkeun Kim, Suha Kwak, Jae Seok Bae, Minkyo Seo, Seunghoon Hong |
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| Rok vydání: | 2021 |
| Předmět: |
Contrast enhancement
Artificial neural network medicine.diagnostic_test Computer science business.industry media_common.quotation_subject Deep learning 020207 software engineering Pattern recognition Computed tomography 02 engineering and technology 030218 nuclear medicine & medical imaging Image synthesis Image (mathematics) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Contrast (vision) Artificial intelligence Medical diagnosis business media_common |
| Zdroj: | WACV |
| Popis: | Contrast materials are often injected into body to contrast specific tissues in Computed Tomography (CT) images. Contrast Enhanced CT (CECT) images obtained in this way are more useful than Non-Enhanced CT (NECT) images for medical diagnosis, but not available for everyone due to side effects of the contrast materials. Motivated by this, we develop a neural network that takes NECT images and generates their CECT counterparts. Learning such a network is extremely challenging since NECT and CECT images for training are not aligned even at the same location of the same patient due to movements of internal organs. We propose a two-stage framework to address this issue. The first stage trains an auxiliary network that removes the effect of contrast enhancement in CECT images to synthesize their NECT counterparts well-aligned with them. In the second stage, the target model is trained to predict the real CECT images given a synthetic NECT image as input. Experimental results and analysis by physicians on abdomen CT images suggest that our method outperforms existing models for neural image synthesis. |
| Databáze: | OpenAIRE |
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