Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2014/479154 |
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| _version_ | 1850110269315874816 |
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| author | Gurman Gill Matthew Toews Reinhard R. Beichel |
| author_facet | Gurman Gill Matthew Toews Reinhard R. Beichel |
| author_sort | Gurman Gill |
| collection | DOAJ |
| description | Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation. |
| format | Article |
| id | doaj-art-45891e23638747fc878148e7df4cd9ec |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-45891e23638747fc878148e7df4cd9ec2025-08-20T02:37:52ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962014-01-01201410.1155/2014/479154479154Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas ApproachGurman Gill0Matthew Toews1Reinhard R. Beichel2Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USABrigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USADepartment of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USAModel-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.http://dx.doi.org/10.1155/2014/479154 |
| spellingShingle | Gurman Gill Matthew Toews Reinhard R. Beichel Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach International Journal of Biomedical Imaging |
| title | Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach |
| title_full | Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach |
| title_fullStr | Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach |
| title_full_unstemmed | Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach |
| title_short | Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach |
| title_sort | robust initialization of active shape models for lung segmentation in ct scans a feature based atlas approach |
| url | http://dx.doi.org/10.1155/2014/479154 |
| work_keys_str_mv | AT gurmangill robustinitializationofactiveshapemodelsforlungsegmentationinctscansafeaturebasedatlasapproach AT matthewtoews robustinitializationofactiveshapemodelsforlungsegmentationinctscansafeaturebasedatlasapproach AT reinhardrbeichel robustinitializationofactiveshapemodelsforlungsegmentationinctscansafeaturebasedatlasapproach |