Projects / Image Analysis / Whole Body Organ Segmentation

Automatic Multi-Organ Segmentation Using Learning-based Segmentation and Level Set Optimization

Abstract

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

Publications and Further Reading

1. Kohlberger, T., Sofka, M., Zhang, J., Birkbeck, N., Wetzl, J., Kaftan, J., Declerck, J., and S. Kevin Zhou, 2011. Automatic Multi-Organ Segmentation Using Learning-based Segmentation and Level Set Optimization. In: Proceedings of the 14th International Conference On Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011). Toronto, Canada.
   abstract bibtex

   We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloudbased shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

   @inproceedings{kohlberger:miccai11,
     author = {Kohlberger, Timo and Sofka, Michal and Zhang, Jingdan and Birkbeck, Neil and Wetzl, Jens and Kaftan, Jens and Declerck, J{\'e}r{\^o}me and and S.~Kevin Zhou},
     title = {Automatic Multi-Organ Segmentation Using Learning-based Segmentation
                   and Level Set Optimization},
     booktitle = {Proceedings of the 14th International Conference on
                       Medical Image Computing and Computer-Assisted
                       Intervention (MICCAI 2011)},
     year = {2011},
     month = "18--22~" # sep,
     address = {Toronto, Canada}
   }