Abstract
This paper proposes a novel method of using regression-guided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel’s deformation to the nearest point on the ROI boundary as well as each voxel’s class label (e.g., ROI versus background). The auto-context model further refines all voxel’s deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.
D. Shen—This work was supported by the National Institute of Health grants 1R01 EB006733.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Satterthwaite, T.D., Davatzikos, C.: Towards an individualized delineation of functional neuroanatomy. Neuron 87, 471–473 (2015)
Aljabar, P., Heckemann, R.A., et al.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46, 726–738 (2009)
Ma, G., Gao, Y., Wu, G., Wu, L., Shen, D.: Atlas-guided multi-channel forest learning for human brain labeling. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Zhang, S., Cai, W.T., Metaxas, D. (eds.) MCV 2014. LNCS, vol. 8848, pp. 97–104. Springer, Heidelberg (2014). Revised Selected Papers
Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint classification-regression forests for spatially structured multi-object segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 870–881. Springer, Heidelberg (2012)
Cootes, T.F., Taylor, C.J., et al.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Cui, X., Liu, Y.e.a.: 3D HAAR-like features for pedestrian detection. In: ICME-2007, pp. 1263–1266. IEEE (2007)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE T-PAMI 32, 1744–1757 (2010)
Brain Development Org: IXIDataSet. http://www.brain-development.org
Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE T-PAMI 35, 611–623 (2013)
Artaechevarria, X., Munoz, A., et al.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE T-MI 28, 1266–1277 (2009)
Coupé, P., Manjón, J.V., et al.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011)
Wu, G., Wang, Q., Zhang, D., Shen, D.: Robust patch-based multi-atlas labeling by joint sparsity regularization. In: MICCAI Workshop STMI (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wu, Z., Park, S.H., Guo, Y., Gao, Y., Shen, D. (2016). Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-47157-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47156-3
Online ISBN: 978-3-319-47157-0
eBook Packages: Computer ScienceComputer Science (R0)