PSNet: Prostate segmentation on MRI based on a convolutional neural network

Zhiqiang Tian, Lizhi Liu, Zhenfeng Zhang, Baowei Fei

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


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.

Original languageEnglish (US)
Article number021208
JournalJournal of Medical Imaging
Issue number2
StatePublished - Apr 1 2018
Externally publishedYes


  • convolutional neural network
  • deep learning
  • magnetic resonance imaging
  • prostate segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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