Abstract
This paper proposes a novel method of using regressionguided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel’s deformation to the nearest point on the ROI boundary as well as each voxel’s class label (e.g., ROI versus background). The auto-context model further refines all voxel’s deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.
| Original language | English |
|---|---|
| Title of host publication | Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings |
| Publisher | Springer Verlag |
| Pages | 237-245 |
| Number of pages | 9 |
| Volume | 10019 LNCS |
| ISBN (Print) | 9783319471563 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 17 → 2016 Oct 17 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10019 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 16/10/17 → 16/10/17 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Regression guided deformable models for segmentation of multiple brain ROIs. / Wu, Zhengwang; Park, Sang Hyun; Guo, Yanrong; Gao, Yaozong; Shen, Dinggang.
Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 10019 LNCS Springer Verlag, 2016. p. 237-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10019 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Regression guided deformable models for segmentation of multiple brain ROIs
AU - Wu, Zhengwang
AU - Park, Sang Hyun
AU - Guo, Yanrong
AU - Gao, Yaozong
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - This paper proposes a novel method of using regressionguided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel’s deformation to the nearest point on the ROI boundary as well as each voxel’s class label (e.g., ROI versus background). The auto-context model further refines all voxel’s deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.
AB - This paper proposes a novel method of using regressionguided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel’s deformation to the nearest point on the ROI boundary as well as each voxel’s class label (e.g., ROI versus background). The auto-context model further refines all voxel’s deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.
UR - http://www.scopus.com/inward/record.url?scp=84992478806&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-47157-0_29
DO - 10.1007/978-3-319-47157-0_29
M3 - Conference contribution
AN - SCOPUS:84992478806
SN - 9783319471563
VL - 10019 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 237
EP - 245
BT - Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings
PB - Springer Verlag
ER -