An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts.
We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0133337 |
| _version_ | 1828960505982615552 |
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| author | Sang Wook Yoo Pamela Guevara Yong Jeong Kwangsun Yoo Joseph S Shin Jean-Francois Mangin Joon-Kyung Seong |
| author_facet | Sang Wook Yoo Pamela Guevara Yong Jeong Kwangsun Yoo Joseph S Shin Jean-Francois Mangin Joon-Kyung Seong |
| author_sort | Sang Wook Yoo |
| collection | DOAJ |
| description | We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively. |
| first_indexed | 2024-12-14T09:28:41Z |
| format | Article |
| id | doaj.art-777c5c5458cf4e4b91aa3881c87dad0f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T09:28:41Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-777c5c5458cf4e4b91aa3881c87dad0f2022-12-21T23:08:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01107e013333710.1371/journal.pone.0133337An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts.Sang Wook YooPamela GuevaraYong JeongKwangsun YooJoseph S ShinJean-Francois ManginJoon-Kyung SeongWe present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.https://doi.org/10.1371/journal.pone.0133337 |
| spellingShingle | Sang Wook Yoo Pamela Guevara Yong Jeong Kwangsun Yoo Joseph S Shin Jean-Francois Mangin Joon-Kyung Seong An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. PLoS ONE |
| title | An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. |
| title_full | An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. |
| title_fullStr | An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. |
| title_full_unstemmed | An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. |
| title_short | An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. |
| title_sort | example based multi atlas approach to automatic labeling of white matter tracts |
| url | https://doi.org/10.1371/journal.pone.0133337 |
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