Abstract
It is important, yet a challenging procedure, to segment bones and cartilages from knee MR images. In this paper, we propose multi-atlas context forests to first segment bones and then segment cartilages. Specifically, for both the bone and cartilage segmentations, we iteratively train sets of random forests, based on training atlas images, to classify the individual voxels. The random forests rely on (1) the appearance features directly computed from images and also (2) the context features associated with tentative segmentation results, generated by the previous layer of random forest in the iterative framework. To extract context features, multiple atlases (with expert segmentation) are first registered, with the tentative segmentation result of the subject under consideration. Then, the spatial priors of anatomical labels of registered atlases are computed and used to calculate context features of the subject. Note that these multi-atlas context features will be iteratively refined based on the (updated) tentative segmentation result of the subject. As better segmentation result leads to more accurate registration between multiple atlases and the subject, context features will become increasingly more useful for the training of subsequent random forests in the iterative framework. As validated by experiments on the SKI10 dataset, our proposed method can achieve high segmentation accuracy.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 186-193 |
| Number of pages | 8 |
| Volume | 9352 |
| ISBN (Print) | 9783319248875 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 2015 Oct 5 → 2015 Oct 5 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9352 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 |
|---|---|
| Country | Germany |
| City | Munich |
| Period | 15/10/5 → 15/10/5 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Multi-atlas context forests for knee MR image segmentation. / Liu, Qin; Wang, Qian; Zhang, Lichi; Gao, Yaozong; Shen, Dinggang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 Springer Verlag, 2015. p. 186-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multi-atlas context forests for knee MR image segmentation
AU - Liu, Qin
AU - Wang, Qian
AU - Zhang, Lichi
AU - Gao, Yaozong
AU - Shen, Dinggang
PY - 2015
Y1 - 2015
N2 - It is important, yet a challenging procedure, to segment bones and cartilages from knee MR images. In this paper, we propose multi-atlas context forests to first segment bones and then segment cartilages. Specifically, for both the bone and cartilage segmentations, we iteratively train sets of random forests, based on training atlas images, to classify the individual voxels. The random forests rely on (1) the appearance features directly computed from images and also (2) the context features associated with tentative segmentation results, generated by the previous layer of random forest in the iterative framework. To extract context features, multiple atlases (with expert segmentation) are first registered, with the tentative segmentation result of the subject under consideration. Then, the spatial priors of anatomical labels of registered atlases are computed and used to calculate context features of the subject. Note that these multi-atlas context features will be iteratively refined based on the (updated) tentative segmentation result of the subject. As better segmentation result leads to more accurate registration between multiple atlases and the subject, context features will become increasingly more useful for the training of subsequent random forests in the iterative framework. As validated by experiments on the SKI10 dataset, our proposed method can achieve high segmentation accuracy.
AB - It is important, yet a challenging procedure, to segment bones and cartilages from knee MR images. In this paper, we propose multi-atlas context forests to first segment bones and then segment cartilages. Specifically, for both the bone and cartilage segmentations, we iteratively train sets of random forests, based on training atlas images, to classify the individual voxels. The random forests rely on (1) the appearance features directly computed from images and also (2) the context features associated with tentative segmentation results, generated by the previous layer of random forest in the iterative framework. To extract context features, multiple atlases (with expert segmentation) are first registered, with the tentative segmentation result of the subject under consideration. Then, the spatial priors of anatomical labels of registered atlases are computed and used to calculate context features of the subject. Note that these multi-atlas context features will be iteratively refined based on the (updated) tentative segmentation result of the subject. As better segmentation result leads to more accurate registration between multiple atlases and the subject, context features will become increasingly more useful for the training of subsequent random forests in the iterative framework. As validated by experiments on the SKI10 dataset, our proposed method can achieve high segmentation accuracy.
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U2 - 10.1007/978-3-319-24888-2_23
DO - 10.1007/978-3-319-24888-2_23
M3 - Conference contribution
AN - SCOPUS:84952060545
SN - 9783319248875
VL - 9352
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 193
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -