ML Anthology

Coupling CRFs and Deformable Models for 3D Medical Image Segmentation

Gabriel Tsechpenakis, Jianhua Wang, Brandon Mayer, Dimitris N. Metaxas
ICCV 2007 pp. 1-8
doi:10.1109/ICCV.2007.4409151 /iccv/2007/tsechpenakis2007iccv-coupling/

Abstract

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with optical coherence tomography.

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Cite

Text

Tsechpenakis et al. "Coupling CRFs and Deformable Models for 3D Medical Image Segmentation." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4409151

Markdown

[Tsechpenakis et al. "Coupling CRFs and Deformable Models for 3D Medical Image Segmentation." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/tsechpenakis2007iccv-coupling/) doi:10.1109/ICCV.2007.4409151

BibTeX

@inproceedings{tsechpenakis2007iccv-coupling,
  title     = {{Coupling CRFs and Deformable Models for 3D Medical Image Segmentation}},
  author    = {Tsechpenakis, Gabriel and Wang, Jianhua and Mayer, Brandon and Metaxas, Dimitris N.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2007},
  pages     = {1-8},
  doi       = {10.1109/ICCV.2007.4409151},
  url       = {https://mlanthology.org/iccv/2007/tsechpenakis2007iccv-coupling/}
}