A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI
Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, co...
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MDPI AG
2020-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/13/3628 |
| _version_ | 1797563931508604928 |
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| author | Yingqian Liu Zhuangzhi Yan |
| author_facet | Yingqian Liu Zhuangzhi Yan |
| author_sort | Yingqian Liu |
| collection | DOAJ |
| description | Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results. |
| first_indexed | 2024-03-10T18:50:11Z |
| format | Article |
| id | doaj.art-398b9ff8a1e84266a35cf38f02fb21ca |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:50:11Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-398b9ff8a1e84266a35cf38f02fb21ca2023-11-20T05:12:17ZengMDPI AGSensors1424-82202020-06-012013362810.3390/s20133628A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRIYingqian Liu0Zhuangzhi Yan1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSegmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.https://www.mdpi.com/1424-8220/20/13/3628deep belief networkshape priorlattice Boltzmann methodhippocampus segmentation |
| spellingShingle | Yingqian Liu Zhuangzhi Yan A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI Sensors deep belief network shape prior lattice Boltzmann method hippocampus segmentation |
| title | A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI |
| title_full | A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI |
| title_fullStr | A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI |
| title_full_unstemmed | A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI |
| title_short | A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI |
| title_sort | combined deep learning and lattice boltzmann model for segmentation of the hippocampus in mri |
| topic | deep belief network shape prior lattice Boltzmann method hippocampus segmentation |
| url | https://www.mdpi.com/1424-8220/20/13/3628 |
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