Multi-atlas and label fusion approach for patient-specific MRI based skull estimation
Purpose MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. Methods The skull is estimated using a multi-atlas segmentation approach. Using a who...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Wiley Blackwell
2017
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| Online Access: | http://hdl.handle.net/1721.1/110713 https://orcid.org/0000-0002-7637-2914 |
| _version_ | 1826192830528225280 |
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| author | Torrado-Carvajal, Angel Herraiz, Joaquin L. Hernandez-Tamames, Juan A. San Jose-Estepar, Raul Eryaman, Yigitcan Rozenholc, Yves Adalsteinsson, Elfar Wald, Lawrence L. Malpica, Norberto |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Torrado-Carvajal, Angel Herraiz, Joaquin L. Hernandez-Tamames, Juan A. San Jose-Estepar, Raul Eryaman, Yigitcan Rozenholc, Yves Adalsteinsson, Elfar Wald, Lawrence L. Malpica, Norberto |
| author_sort | Torrado-Carvajal, Angel |
| collection | MIT |
| description | Purpose
MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.
Methods
The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.
Results
The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.
Conclusion
It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. |
| first_indexed | 2024-09-23T09:29:49Z |
| format | Article |
| id | mit-1721.1/110713 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:29:49Z |
| publishDate | 2017 |
| publisher | Wiley Blackwell |
| record_format | dspace |
| spelling | mit-1721.1/1107132022-09-26T11:49:53Z Multi-atlas and label fusion approach for patient-specific MRI based skull estimation Torrado-Carvajal, Angel Herraiz, Joaquin L. Hernandez-Tamames, Juan A. San Jose-Estepar, Raul Eryaman, Yigitcan Rozenholc, Yves Adalsteinsson, Elfar Wald, Lawrence L. Malpica, Norberto Institute for Medical Engineering and Science Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Adalsteinsson, Elfar Purpose MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. Methods The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. Results The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. Conclusion It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. 2017-07-14T19:40:20Z 2017-07-14T19:40:20Z 2016-03 2015-03 Article http://purl.org/eprint/type/JournalArticle 0740-3194 1522-2594 http://hdl.handle.net/1721.1/110713 Torrado-Carvajal, Angel; Herraiz, Joaquin L.; Hernandez-Tamames, Juan A. et al. “Multi-Atlas and Label Fusion Approach for Patient-Specific MRI Based Skull Estimation.” Magnetic Resonance in Medicine 75, 4 (May 2015): 1797–1807 © 2015 Wiley Periodicals, Inc https://orcid.org/0000-0002-7637-2914 en_US http://dx.doi.org/10.1002/mrm.25737 Magnetic Resonance in Medicine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Blackwell Other repository |
| spellingShingle | Torrado-Carvajal, Angel Herraiz, Joaquin L. Hernandez-Tamames, Juan A. San Jose-Estepar, Raul Eryaman, Yigitcan Rozenholc, Yves Adalsteinsson, Elfar Wald, Lawrence L. Malpica, Norberto Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title | Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title_full | Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title_fullStr | Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title_full_unstemmed | Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title_short | Multi-atlas and label fusion approach for patient-specific MRI based skull estimation |
| title_sort | multi atlas and label fusion approach for patient specific mri based skull estimation |
| url | http://hdl.handle.net/1721.1/110713 https://orcid.org/0000-0002-7637-2914 |
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