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Proceedings Paper

Automatic MR prostate segmentation by deep learning with holistically-nested networks
Author(s): Ruida Cheng; Holger R. Roth; Nathan Lay; Le Lu; Baris Turkbey; William Gandler; Evan S. McCreedy; Peter Choyke; Ronald M. Summers; Matthew J. McAuliffe
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Paper Abstract

Accurate automatic prostate magnetic resonance image (MRI) segmentation is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. The proposed method performs end-to- end segmentation by integrating holistically nested edge detection with fully convolutional neural networks. Holistically-nested networks (HNN) automatically learn the hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 247 patients in 5-fold cross-validation. We achieve a mean Dice Similarity Coefficient of 88.70% and a mean Jaccard Similarity Coefficient of 80.29% without trimming any erroneous contours at apex and base.

Paper Details

Date Published: 24 February 2017
PDF: 6 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332H (24 February 2017); doi: 10.1117/12.2254558
Show Author Affiliations
Ruida Cheng, National Institutes of Health (United States)
Holger R. Roth, National Institutes of Health (United States)
Nathan Lay, National Institutes of Health (United States)
Le Lu, National Institutes of Health (United States)
Baris Turkbey, National Institutes of Health (United States)
William Gandler, National Institutes of Health (United States)
Evan S. McCreedy, National Institutes of Health (United States)
Peter Choyke, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)
Matthew J. McAuliffe, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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