A gpu framework for parallel segmentation of volumetric images using discrete deformable models

Although research in image segmentation has been very active during the last decades, it is still a very challenging problem. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.

@InProceedings{SIGM10,
  author       = {Schmid, J. and Iglesias Guitián, J. and Gobbetti, E. and Magnenat-Thalmann, N.},
  title        = {A gpu framework for parallel segmentation of volumetric images using discrete deformable models},
  booktitle    = {Proc. 3DAnatomicalHuman Summer School},
  year         = {2010},
  keywords     = {gpu computing, volume segmentation},
  url          = {https://publications.crs4.it/pubdocs/2010/SIGM10},
}