Unsupervised Three-Dimensional Image Registration Using a Cycle Convolutional Neural Network
| Autor: | Xiaomei Zhu, Tiancong Hua, Youyong Kong, Huazhong Shu, Guanyu Yang, Lijun Tang, Ziwei Lu, Jean-Louis Coatrieux, Liyu Hu, Jean-Louis Dillenseger |
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| Přispěvatelé: | Laboratory of Image Science and Technology [Nanjing] (LIST), Southeast University [Jiangsu]-School of Computer Science and Engineering, Nanjing Medical University, Centre de Recherche en Information Biomédicale sino-français (CRIBS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Southeast University [Jiangsu]-Institut National de la Santé et de la Recherche Médicale (INSERM), Dillenseger, Jean-Louis, Université de Rennes (UR)-Southeast University [Jiangsu]-Institut National de la Santé et de la Recherche Médicale (INSERM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION [INFO.INFO-IM] Computer Science [cs]/Medical Imaging Image registration convolutional neural network 010501 environmental sciences 01 natural sciences Convolutional neural network Displacement (vector) Field (computer science) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Medical imaging [INFO.INFO-IM]Computer Science [cs]/Medical Imaging unsupervised 0105 earth and related environmental sciences [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing [SDV.IB] Life Sciences [q-bio]/Bioengineering Artificial neural network business.industry Deep learning Pattern recognition Image segmentation non-rigid [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging [SDV.IB]Life Sciences [q-bio]/Bioengineering Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 3D |
| Zdroj: | 2019 IEEE International Conference on Image Processing (ICIP) 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.2174-2178, ⟨10.1109/ICIP.2019.8803163⟩ ICIP |
| DOI: | 10.1109/ICIP.2019.8803163⟩ |
| Popis: | International audience; In this paper, an unsupervised cycle image registration convolutional neural network named CIRNet is developed for 3D medical image registration. Different from most deep learning based registration methods that require known spatial transforms, our proposed method is trained in an unsu-pervised way and predicts the dense displacement vector field. The CIRNet is composed by two image registration modules which have the same architecture and share the parameters. A cycle identical loss is designed in the CIRNet to provide additional constraints to ensure the accuracy of the predicted dense displacement vector field. The method is evaluated by the registration in 4D (3D+t) cardiac CT and MRI images respectively. Quantitative evaluation results demonstrate that our method performs better than the other two existing image registration algorithms. Especially, compared to the traditional image registration methods, our proposed network can finish 3D image registration in less than one second. |
| Databáze: | OpenAIRE |
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