An automated, geometry-based method for hippocampal shape and thickness analysis
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 summ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923003336 |
| _version_ | 1797799059236323328 |
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| author | Kersten Diers Hannah Baumeister Frank Jessen Emrah Düzel David Berron Martin Reuter |
| author_facet | Kersten Diers Hannah Baumeister Frank Jessen Emrah Düzel David Berron Martin Reuter |
| author_sort | Kersten Diers |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:14:16Z |
| format | Article |
| id | doaj.art-1cad3cc6621b48fa8074cd6433d9db95 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-13T04:14:16Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-1cad3cc6621b48fa8074cd6433d9db952023-06-21T06:51:08ZengElsevierNeuroImage1095-95722023-08-01276120182An automated, geometry-based method for hippocampal shape and thickness analysisKersten Diers0Hannah Baumeister1Frank Jessen2Emrah Düzel3David Berron4Martin Reuter5AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, GermanyClinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, GermanyClinical 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, GermanyClinical 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 KingdomClinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, SwedenCorresponding author.; 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, USAThe 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.http://www.sciencedirect.com/science/article/pii/S1053811923003336Shape analysisHippocampusThicknessNeuroimagingFlattening |
| spellingShingle | Kersten Diers Hannah Baumeister Frank Jessen Emrah Düzel David Berron Martin Reuter An automated, geometry-based method for hippocampal shape and thickness analysis NeuroImage Shape analysis Hippocampus Thickness Neuroimaging Flattening |
| title | An automated, geometry-based method for hippocampal shape and thickness analysis |
| title_full | An automated, geometry-based method for hippocampal shape and thickness analysis |
| title_fullStr | An automated, geometry-based method for hippocampal shape and thickness analysis |
| title_full_unstemmed | An automated, geometry-based method for hippocampal shape and thickness analysis |
| title_short | An automated, geometry-based method for hippocampal shape and thickness analysis |
| title_sort | automated geometry based method for hippocampal shape and thickness analysis |
| topic | Shape analysis Hippocampus Thickness Neuroimaging Flattening |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923003336 |
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