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Zhang J, Fu T, Xiao D, Fan J, Song H, Ai D, Yang J. Bi-Fusion of Structure and Deformation at Multi-Scale for Joint Segmentation and Registration. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:3676-3691. [PMID: 38837936 DOI: 10.1109/tip.2024.3407657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Medical image segmentation and registration are two fundamental and highly related tasks. However, current works focus on the mutual promotion between the two at the loss function level, ignoring the feature information generated by the encoder-decoder network during the task-specific feature mapping process and the potential inter-task feature relationship. This paper proposes a unified multi-task joint learning framework based on bi-fusion of structure and deformation at multi-scale, called BFM-Net, which simultaneously achieves the segmentation results and deformation field in a single-step estimation. BFM-Net consists of a segmentation subnetwork (SegNet), a registration subnetwork (RegNet), and the multi-task connection module (MTC). The MTC module is used to transfer the latent feature representation between segmentation and registration at multi-scale and link different tasks at the network architecture level, including the spatial attention fusion module (SAF), the multi-scale spatial attention fusion module (MSAF) and the velocity field fusion module (VFF). Extensive experiments on MR, CT and ultrasound images demonstrate the effectiveness of our approach. The MTC module can increase the Dice scores of segmentation and registration by 3.2%, 1.6%, 2.2%, and 6.2%, 4.5%, 3.0%, respectively. Compared with six state-of-the-art algorithms for segmentation and registration, BFM-Net can achieve superior performance in various modal images, fully demonstrating its effectiveness and generalization.
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Li Y, Xie L, Khandelwal P, Wisse LEM, Brown CA, Prabhakaran K, Dylan Tisdall M, Mechanic-Hamilton D, Detre JA, Das SR, Wolk DA, Yushkevich PA. Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.595190. [PMID: 38826413 PMCID: PMC11142184 DOI: 10.1101/2024.05.21.595190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. Method MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. Results The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. Conclusion The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.
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Affiliation(s)
- Yue Li
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Long Xie
- Department of Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | | | | | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Wuestefeld A, Binette AP, van Westen D, Strandberg O, Stomrud E, Mattsson-Carlgren N, Janelidze S, Smith R, Palmqvist S, Baumeister H, Berron D, Yushkevich PA, Hansson O, Spotorno N, Wisse LEM. Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.594976. [PMID: 38826333 PMCID: PMC11142072 DOI: 10.1101/2024.05.21.594976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. Methods BioFINDER-2 participants with memory impairment, abnormal amyloid-β status and tau-PET were included. Forty-one EOAD individuals aged ≤65 years and, as comparison, late-onset AD (LOAD, ≥70 years, n=154) and Aβ-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. Results AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. Conclusions We found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
- Image and Function, Skåne University Hospital, 22242 Lund Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Department of Neurology, Skåne University Hospital, 22242 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184 Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
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Fan G, Li Y, Wang D, Zhang J, Du X, Liu H, Liao X. Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms. J Appl Clin Med Phys 2024:e14378. [PMID: 38729652 DOI: 10.1002/acm2.14378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. METHODS A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. RESULTS The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805). CONCLUSIONS A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jianjin Zhang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiaokang Du
- Department of Orthopedics, The People's Hospital of Wenshang County, Wenshang, Shandong, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua PearlRiverDelta, Guangzhou, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
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Liu H, Nie D, Yang J, Wang J, Tang Z. A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping. IEEE J Biomed Health Inform 2024; 28:1484-1493. [PMID: 38113158 DOI: 10.1109/jbhi.2023.3344646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation. In most existing DL-MA methods, such correspondence is usually established using traditional or deep learning based registration methods at image level with no further feature level adaption. This could cause possible atlas-target feature inconsistency. As a result, the information from atlases often has limited positive and even counteractive impact on the final segmentation results. To tackle this issue, in this paper, we propose a new DL-MA framework, where a novel differentiable atlas feature warping module with a new smooth regularization term is presented to establish feature level atlas-target correspondence. Comparing with the existing DL-MA methods, in our framework, atlas features containing anatomical prior knowledge are more relevant to the target image feature, leading the final segmentation results to a high accuracy level. We evaluate our framework in the context of brain parcellation using two public MR brain image datasets: LPBA40 and NIREP-NA0. The experimental results demonstrate that our framework outperforms both traditional multi-atlas segmentation (MAS) and state-of-the-art DL-MA methods with statistical significance. Further ablation studies confirm the effectiveness of the proposed differentiable atlas feature warping module.
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Wuestefeld A, Pichet Binette A, Berron D, Spotorno N, van Westen D, Stomrud E, Mattsson-Carlgren N, Strandberg O, Smith R, Palmqvist S, Glenn T, Moes S, Honer M, Arfanakis K, Barnes LL, Bennett DA, Schneider JA, Wisse LEM, Hansson O. Age-related and amyloid-beta-independent tau deposition and its downstream effects. Brain 2023; 146:3192-3205. [PMID: 37082959 PMCID: PMC10393402 DOI: 10.1093/brain/awad135] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Amyloid-β (Aβ) is hypothesized to facilitate the spread of tau pathology beyond the medial temporal lobe. However, there is evidence that, independently of Aβ, age-related tau pathology might be present outside of the medial temporal lobe. We therefore aimed to study age-related Aβ-independent tau deposition outside the medial temporal lobe in two large cohorts and to investigate potential downstream effects of this on cognition and structural measures. We included 545 cognitively unimpaired adults (40-92 years) from the BioFINDER-2 study (in vivo) and 639 (64-108 years) from the Rush Alzheimer's Disease Center cohorts (ex vivo). 18F-RO948- and 18F-flutemetamol-PET standardized uptake value ratios were calculated for regional tau and global/regional Aβ in vivo. Immunohistochemistry was used to estimate Aβ load and tangle density ex vivo. In vivo medial temporal lobe volumes (subiculum, cornu ammonis 1) and cortical thickness (entorhinal cortex, Brodmann area 35) were obtained using Automated Segmentation for Hippocampal Subfields packages. Thickness of early and late neocortical Alzheimer's disease regions was determined using FreeSurfer. Global cognition and episodic memory were estimated to quantify cognitive functioning. In vivo age-related tau deposition was observed in the medial temporal lobe and in frontal and parietal cortical regions, which was statistically significant when adjusting for Aβ. This was also observed in individuals with low Aβ load. Tau deposition was negatively associated with cortical volumes and thickness in temporal and parietal regions independently of Aβ. The associations between age and cortical volume or thickness were partially mediated via tau in regions with early Alzheimer's disease pathology, i.e. early tau and/or Aβ pathology (subiculum/Brodmann area 35/precuneus/posterior cingulate). Finally, the associations between age and cognition were partially mediated via tau in Brodmann area 35, even when including Aβ-PET as covariate. Results were validated in the ex vivo cohort showing age-related and Aβ-independent increases in tau aggregates in and outside the medial temporal lobe. Ex vivo age-cognition associations were mediated by medial and inferior temporal tau tangle density, while correcting for Aβ density. Taken together, our study provides support for primary age-related tauopathy even outside the medial temporal lobe in vivo and ex vivo, with downstream effects on structure and cognition. These results have implications for our understanding of the spreading of tau outside the medial temporal lobe, also in the context of Alzheimer's disease. Moreover, this study suggests the potential utility of tau-targeting treatments in primary age-related tauopathy, likely already in preclinical stages in individuals with low Aβ pathology.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
- Image and Function, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Svenja Moes
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Michael Honer
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Lisa L Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Julie A Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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Diers K, Baumeister H, Jessen F, Düzel E, Berron D, Reuter M. An automated, geometry-based method for hippocampal shape and thickness analysis. Neuroimage 2023; 276:120182. [PMID: 37230208 DOI: 10.1016/j.neuroimage.2023.120182] [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: 02/01/2023] [Revised: 04/29/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023] Open
Abstract
The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.
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Affiliation(s)
- Kersten Diers
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Hannah Baumeister
- Clinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Frank Jessen
- Clinical Alzheimer's Disease Research, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Emrah Düzel
- Clinical Neurophysiology and Memory Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - David Berron
- Clinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA; Department of Radiology, Harvard Medical School, Boston MA, USA.
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Yildirim Z, Delen F, Berron D, Baumeister H, Ziegler G, Schütze H, Glanz W, Dobisch L, Peters O, Freiesleben SD, Schneider LS, Priller J, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Schott BH, Meiberth D, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Kilimann I, Laske C, Munk MH, Spottke A, Roy N, Heneka M, Brosseron F, Wagner M, Roeske S, Ramirez A, Ewers M, Dechent P, Hetzer S, Scheffler K, Kleineidam L, Wolfsgruber S, Yakupov R, Schmid M, Berger M, Gurvit H, Jessen F, Duzel E. Brain reserve contributes to distinguishing preclinical Alzheimer's stages 1 and 2. Alzheimers Res Ther 2023; 15:43. [PMID: 36855049 PMCID: PMC9972621 DOI: 10.1186/s13195-023-01187-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/08/2023] [Indexed: 03/02/2023]
Abstract
BACKGROUND In preclinical Alzheimer's disease, it is unclear why some individuals with amyloid pathologic change are asymptomatic (stage 1), whereas others experience subjective cognitive decline (SCD, stage 2). Here, we examined the association of stage 1 vs. stage 2 with structural brain reserve in memory-related brain regions. METHODS We tested whether the volumes of hippocampal subfields and parahippocampal regions were larger in individuals at stage 1 compared to asymptomatic amyloid-negative older adults (healthy controls, HCs). We also tested whether individuals with stage 2 would show the opposite pattern, namely smaller brain volumes than in amyloid-negative individuals with SCD. Participants with cerebrospinal fluid (CSF) biomarker data and bilateral volumetric MRI data from the observational, multi-centric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study were included. The sample comprised 95 amyloid-negative and 26 amyloid-positive asymptomatic participants as well as 104 amyloid-negative and 47 amyloid-positive individuals with SCD. Volumes were based on high-resolution T2-weighted images and automatic segmentation with manual correction according to a recently established high-resolution segmentation protocol. RESULTS In asymptomatic individuals, brain volumes of hippocampal subfields and of the parahippocampal cortex were numerically larger in stage 1 compared to HCs, whereas the opposite was the case in individuals with SCD. MANOVAs with volumes as dependent data and age, sex, years of education, and DELCODE site as covariates showed a significant interaction between diagnosis (asymptomatic versus SCD) and amyloid status (Aß42/40 negative versus positive) for hippocampal subfields. Post hoc paired comparisons taking into account the same covariates showed that dentate gyrus and CA1 volumes in SCD were significantly smaller in amyloid-positive than negative individuals. In contrast, CA1 volumes were significantly (p = 0.014) larger in stage 1 compared with HCs. CONCLUSIONS These data indicate that HCs and stages 1 and 2 do not correspond to linear brain volume reduction. Instead, stage 1 is associated with larger than expected volumes of hippocampal subfields in the face of amyloid pathology. This indicates a brain reserve mechanism in stage 1 that enables individuals with amyloid pathologic change to be cognitively normal and asymptomatic without subjective cognitive decline.
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Affiliation(s)
- Zerrin Yildirim
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Vakif Gureba Cad., Capa Kampusu Sehremini, Fatih, 34093, Istanbul, Turkey.
- Department of Neurology, Bagcilar Training and Research Hospital, University of Health Sciences, 34200, Istanbul, Turkey.
| | - Firuze Delen
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Vakif Gureba Cad., Capa Kampusu Sehremini, Fatih, 34093, Istanbul, Turkey
- Department of Neurology, Basaksehir Cam and Sakura City Hospital, 34480, Istanbul, Turkey
| | - David Berron
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Hartmut Schütze
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Silka Dawn Freiesleben
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Luisa-Sophie Schneider
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Jens Wiltfang
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075, Goettingen, Germany
| | - Björn-Hendrik Schott
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075, Goettingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Dix Meiberth
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924, Cologne, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377, Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Department of Neuroradiology, University Hospital LMU, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147, Rostock, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Gehlsheimer Str. 20, 18147, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
| | - Michael Heneka
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931, Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377, Munich, Germany
| | - Peter Dechent
- Department of Cognitive Neurology, MR-Research in Neurosciences, Georg-August-University Goettingen, Göttingen, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076, Tübingen, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Institute for Medical Biometry Informatics, and Epidemiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry Informatics, and Epidemiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Hakan Gurvit
- Department of Neurology, Behavioral Neurology and Movement Disorders Unit, Istanbul Faculty of Medicine, Istanbul University, 34093, Istanbul, Turkey
- Neuroimaging Unit, Istanbul University, Hulusi Behcet Life Sciences Research Center, 34093, Istanbul, Turkey
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931, Cologne, Germany
| | - Emrah Duzel
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
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9
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Diwakar M, Singh P, Ravi V, Maurya A. A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion. Diagnostics (Basel) 2023; 13:diagnostics13050820. [PMID: 36899965 PMCID: PMC10000748 DOI: 10.3390/diagnostics13050820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/29/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.
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Affiliation(s)
- Manoj Diwakar
- Department of CSE, Graphic Era Deemed to be University, Dehradun 248002, India
| | - Prabhishek Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
- Correspondence:
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
| | - Ankur Maurya
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
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