Applications of Marginal Space Learning in Medical Imaging

Author:

Zheng Yefeng,Comaniciu Dorin

Publisher

Springer New York

Reference84 articles.

1. Barbu, A., Shling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D.: Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans. Medical Imaging 31(2), 240–250 (2012)

2. Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D.: Automatic detection and segmentation of axillary lymph nodes. In: Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, vol. 1, pp. 28–36 (2010)

3. Birkbeck, N., Zhang, J., Zhou, S.K.: Region-specific hierarchical segmentation of MR prostate using discriminative learning. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, pp. 4–11 (2012)

4. Carneiro, G., Amat, F., Georgescu, B., Good, S., Comaniciu, D.: Semantic-based indexing of fetal anatomies from 3-D ultrasound data using global/semi-local context and sequential sampling. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–8 (2008)

5. Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Trans. Medical Imaging 27(9), 1342–1355 (2008)

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