Abstract
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. . First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. . Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. . Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.
| Original language | English |
|---|---|
| Journal | Neurocomputing |
| DOIs | |
| Publication status | Accepted/In press - 2016 Jan 15 |
Fingerprint
Keywords
- Atlas selection
- Brain MR images
- Clustering
- Image segmentation
- Random forest
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images. / Zhang, Lichi; Wang, Qian; Gao, Yaozong; Li, Hongxin; Wu, Guorong; Shen, Dinggang.
In: Neurocomputing, 15.01.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
AU - Zhang, Lichi
AU - Wang, Qian
AU - Gao, Yaozong
AU - Li, Hongxin
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2016/1/15
Y1 - 2016/1/15
N2 - Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. . First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. . Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. . Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.
AB - Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. . First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. . Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. . Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.
KW - Atlas selection
KW - Brain MR images
KW - Clustering
KW - Image segmentation
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85003520708&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85003520708&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2016.05.082
DO - 10.1016/j.neucom.2016.05.082
M3 - Article
AN - SCOPUS:85003520708
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -