Skip to main content

Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10019))

Included in the following conference series:

  • 1982 Accesses


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

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Satterthwaite, T.D., Davatzikos, C.: Towards an individualized delineation of functional neuroanatomy. Neuron 87, 471–473 (2015)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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

    Google Scholar 

  4. 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)

    Chapter  Google Scholar 

  5. Cootes, T.F., Taylor, C.J., et al.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)

    Article  Google Scholar 

  6. Cui, X., Liu, Y.e.a.: 3D HAAR-like features for pedestrian detection. In: ICME-2007, pp. 1263–1266. IEEE (2007)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Brain Development Org: IXIDataSet.

  9. 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)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. Coupé, P., Manjón, J.V., et al.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011)

    Article  Google Scholar 

  12. Wu, G., Wang, Q., Zhang, D., Shen, D.: Robust patch-based multi-atlas labeling by joint sparsity regularization. In: MICCAI Workshop STMI (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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.

Download citation

  • DOI:

  • 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)

Publish with us

Policies and ethics