17 January 2018 PSNet: prostate segmentation on MRI based on a convolutional neural network
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Abstract
Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of 85.0 ± 3.8 % as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Zhiqiang Tian, Zhiqiang Tian, Lizhi Liu, Lizhi Liu, Zhenfeng Zhang, Zhenfeng Zhang, Baowei Fei, Baowei Fei, } "PSNet: prostate segmentation on MRI based on a convolutional neural network," Journal of Medical Imaging 5(2), 021208 (17 January 2018). https://doi.org/10.1117/1.JMI.5.2.021208 . Submission: Received: 21 September 2017; Accepted: 20 December 2017
Received: 21 September 2017; Accepted: 20 December 2017; Published: 17 January 2018
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