Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917594946076672 |
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| author | Guo, Lianghu Tao, Tianli Cai, Xinyi Zhu, Zihao Huang, Jiawei Zhu, Lixuan Gu, Zhuoyang Tang, Haifeng Zhou, Rui Han, Siyan Liang, Yan Yang, Qing Shen, Dinggang Zhang, Han |
| author_facet | Guo, Lianghu Tao, Tianli Cai, Xinyi Zhu, Zihao Huang, Jiawei Zhu, Lixuan Gu, Zhuoyang Tang, Haifeng Zhou, Rui Han, Siyan Liang, Yan Yang, Qing Shen, Dinggang Zhang, Han |
| contents | Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion Guo, Lianghu Tao, Tianli Cai, Xinyi Zhu, Zihao Huang, Jiawei Zhu, Lixuan Gu, Zhuoyang Tang, Haifeng Zhou, Rui Han, Siyan Liang, Yan Yang, Qing Shen, Dinggang Zhang, Han Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field. |
| title | Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.13776 |