Deep learning with orthogonal volumetric HED segmentation and 3D surface reconstruction model of prostate MRI

@article{Cheng2017DeepLW,
  title={Deep learning with orthogonal volumetric HED segmentation and 3D surface reconstruction model of prostate MRI},
  author={Ruida Cheng and Nathan Lay and Francesca Mertan and Baris Turkbey and Holger Roth and Le Lu and William Gandler and Evan S. McCreedy and Thomas J Pohida and Peter L. Choyke and Matthew J. McAuliffe and Ronald M. Summers},
  journal={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)},
  year={2017},
  pages={749-753}
}
Automatic MR whole prostate segmentation is a challenging task. Recent approaches have attempted to harness the capabilities of deep learning for MR prostate segmentation to tackle pixel-level labeling tasks. Patch-based and hierarchical features-based deep CNN models were used to delineate the prostate boundary. To further investigate this problem, we introduce a Holistically-Nested Edge Detector (HED) MRI prostate deep learning segmentation and 3D surface reconstruction model that facilitate… CONTINUE READING

Citations

Publications citing this paper.

Automatic high resolution segmentation of the prostate from multi-planar MRI

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) • 2018
View 3 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…