Contour-Driven Atlas-Based Segmentation
We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- a...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/111005 https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1811080943617703936 |
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| author | Wachinger, Christian Fritscher, Karl Sharp, Greg Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Wachinger, Christian Fritscher, Karl Sharp, Greg Golland, Polina |
| author_sort | Wachinger, Christian |
| collection | MIT |
| description | We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. |
| first_indexed | 2024-09-23T11:39:19Z |
| format | Article |
| id | mit-1721.1/111005 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:39:19Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1110052022-10-01T05:04:09Z Contour-Driven Atlas-Based Segmentation Wachinger, Christian Fritscher, Karl Sharp, Greg Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wachinger, Christian Golland, Polina We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. 2017-08-23T18:51:08Z 2017-08-23T18:51:08Z 2015-12 2015-06 Article http://purl.org/eprint/type/JournalArticle 0278-0062 1558-254X http://hdl.handle.net/1721.1/111005 Wachinger, Christian et al. “Contour-Driven Atlas-Based Segmentation.” IEEE Transactions on Medical Imaging 34, 12 (December 2015): 2492–2505 © 2015 Institute of Electrical and Electronics Engineers (IEEE) https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1109/TMI.2015.2442753 IEEE Transactions on Medical Imaging Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PMC |
| spellingShingle | Wachinger, Christian Fritscher, Karl Sharp, Greg Golland, Polina Contour-Driven Atlas-Based Segmentation |
| title | Contour-Driven Atlas-Based Segmentation |
| title_full | Contour-Driven Atlas-Based Segmentation |
| title_fullStr | Contour-Driven Atlas-Based Segmentation |
| title_full_unstemmed | Contour-Driven Atlas-Based Segmentation |
| title_short | Contour-Driven Atlas-Based Segmentation |
| title_sort | contour driven atlas based segmentation |
| url | http://hdl.handle.net/1721.1/111005 https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X |
| work_keys_str_mv | AT wachingerchristian contourdrivenatlasbasedsegmentation AT fritscherkarl contourdrivenatlasbasedsegmentation AT sharpgreg contourdrivenatlasbasedsegmentation AT gollandpolina contourdrivenatlasbasedsegmentation |