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Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images

Authors
Zhang, LichiWang, QianGao, YaozongLi, HongxinWu, GuorongShen, Dinggang
Issue Date
15-3월-2017
Publisher
ELSEVIER SCIENCE BV
Keywords
Image segmentation; Random forest; Brain MR images; Atlas selection; Clustering
Citation
NEUROCOMPUTING, v.229, pp.3 - 12
Indexed
SCIE
SCOPUS
Journal Title
NEUROCOMPUTING
Volume
229
Start Page
3
End Page
12
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/84145
DOI
10.1016/j.neucom.2016.05.082
ISSN
0925-2312
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 hippo campus, 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. (C) 2016 Elsevier B.V. All rights reserved.
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