Topological Correction of Infant White Matter Surfaces Using Anatomically Constrained Convolutional Neural Network
Autor: | Dinggang Shen, Li Wang, Gang Li, Chunfeng Lian, Wei Shao, Liang Sun, Zhengwang Wu, Daoqiang Zhang, Weili Lin |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Brain development
Level set method Computer science Cognitive Neuroscience Brain tissue Topology computer.software_genre Convolutional neural network 050105 experimental psychology Article Topological defect White matter 03 medical and health sciences 0302 clinical medicine Voxel medicine Image Processing Computer-Assisted Animals Humans 0501 psychology and cognitive sciences Brain Mapping business.industry Deep learning 05 social sciences Brain Infant Reproducibility of Results Magnetic Resonance Imaging White Matter medicine.anatomical_structure Neurology Macaca Artificial intelligence Neural Networks Computer business Artifacts computer 030217 neurology & neurosurgery |
Zdroj: | Neuroimage |
Popis: | Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method. |
Databáze: | OpenAIRE |
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