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Robust brain ROI segmentation by deformation regression and deformable shape model

Title
Robust brain ROI segmentation by deformation regression and deformable shape model
Authors
Wu, ZhengwangGuo, YanrongPark, Sang HyunGao, YaozongDong, PeiLee, Seong WhanShen, Dinggang
DGIST Authors
Park, Sang Hyun
Issue Date
2018-01
Citation
Medical Image Analysis, 43, 198-213
Type
Article
Article Type
Article
Keywords
IMAGE SEGMENTATIONAUTOMATIC SEGMENTATIONLABEL PROPAGATIONATLAS SELECTIONFUSIONCLASSIFICATIONHIPPOCAMPUSSTRATEGIESFRAMEWORKFORESTS
ISSN
1361-8415
Abstract
We propose a robust and efficient learning-based deformable model for segmenting regions of interest (ROIs) from structural MR brain images. Different from the conventional deformable-model-based methods that deform a shape model locally around the initialization location, we learn an image-based regressor to guide the deformable model to fit for the target ROI. Specifically, given any voxel in a new image, the image-based regressor can predict the displacement vector from this voxel towards the boundary of target ROI, which can be used to guide the deformable segmentation. By predicting the displacement vector maps for the whole image, our deformable model is able to use multiple non-boundary predictions to jointly determine and iteratively converge the initial shape model to the target ROI boundary, which is more robust to the local prediction error and initialization. In addition, by introducing the prior shape model, our segmentation avoids the isolated segmentations as often occurred in the previous multi-atlas based methods. In order to learn an image-based regressor for displacement vector prediction, we adopt the following novel strategies in the learning procedure: (1) a joint classification and regression random forest is proposed to learn an image-based regressor together with an ROI classifier in a multi-task manner; (2) high-level context features are extracted from intermediate (estimated) displacement vector and classification maps to enforce the relationship between predicted displacement vectors at neighboring voxels. To validate our method, we compare it with the state-of-the-art multi-atlas-based methods and other learning-based methods on three public brain MR datasets. The results consistently show that our method is better in terms of both segmentation accuracy and computational efficiency. (C) 2017 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/5608
DOI
10.1016/j.media.2017.11.001
Publisher
ELSEVIER SCIENCE BV
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab1. Journal Articles


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