Incremental Shape Statistics Learning for Prostate Tracking in TRUS

@article{Yan2010IncrementalSS,
  title={Incremental Shape Statistics Learning for Prostate Tracking in TRUS},
  author={Pingkun Yan and Jochen Kruecker},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2010},
  volume={13 Pt 2},
  pages={
          42-9
        }
}
Automatic delineation of the prostate boundary in transrectal ultrasound (TRUS) can play a key role in image-guided prostate intervention. However, it is a very challenging task for several reasons, especially due to the large variation of the prostate shape from the base to the apex. To deal with the problem, a new method for incrementally learning the patient-specific local shape statistics is proposed in this paper to help achieve robust and accurate boundary delineation over the entire… CONTINUE READING

Citations

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A survey of prostate modeling for image analysis

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Towards robust and effective shape modeling: Sparse shape composition

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Adaptively Learning Local Shape Statistics for Prostate Segmentation in Ultrasound

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