21 August 2017 Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks
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Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of 89.77%±3.29% and a mean Jaccard similarity coefficient (IoU) of 81.59%±5.18% are used to calculate without trimming any end slices. The proposed holistic model significantly (p<0.001) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ruida Cheng, Holger R. Roth, Nathan S. Lay, Le Lu, Baris Turkbey, William Gandler, Evan S. McCreedy, Thomas J. Pohida, Peter A. Pinto, Peter L. Choyke, Matthew J. McAuliffe, Ronald M. Summers, "Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks," Journal of Medical Imaging 4(4), 041302 (21 August 2017). https://doi.org/10.1117/1.JMI.4.4.041302 . Submission: Received: 7 March 2017; Accepted: 22 May 2017
Received: 7 March 2017; Accepted: 22 May 2017; Published: 21 August 2017

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