title: Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images.
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.
authors.affiliation: Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University.
authors.forename: Lichi
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Computer Science, University of North Carolina at Chapel Hill.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
keywords: Image segmentation; atlas selection; brain MR images; clustering; random forest