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Liu Y, Wang M, Yu X, Han Y, Jiang J, Yan Z. An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation. Med Phys 2024; 51:4105-4120. [PMID: 38373278 DOI: 10.1002/mp.16984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/19/2023] [Accepted: 01/24/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance. OBJECTIVE Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression of AD. METHODS We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets. RESULTS The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively. CONCLUSIONS Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD.
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Affiliation(s)
- Yingqian Liu
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
- School of Electrical Engineering, Shandong University of Aeronautics, Binzhou, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Zhuangzhi Yan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
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Chen T, You W, Zhang L, Ye W, Feng J, Lu J, Lv J, Tang Y, Wei D, Gui S, Jiang J, Wang Z, Wang Y, Zhao Q, Zhang Y, Qu J, Li C, Jiang Y, Zhang X, Li Y, Guan S. Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography. Front Physiol 2024; 14:1310357. [PMID: 38239880 PMCID: PMC10794642 DOI: 10.3389/fphys.2023.1310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/28/2023] [Indexed: 01/22/2024] Open
Abstract
Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.
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Affiliation(s)
- Ting Chen
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Wei You
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Liyuan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wanxing Ye
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junqiang Feng
- Department of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jing Lu
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jian Lv
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yudi Tang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Dachao Wei
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Siming Gui
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jia Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ziyao Wang
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanwen Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qi Zhao
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yifan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuhua Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Youxiang Li
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Sheng Guan
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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