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.
Original language | English (US) |
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Article number | 021208 |
Journal | Journal of Medical Imaging |
Volume | 5 |
Issue number | 2 |
DOIs | |
State | Published - Apr 1 2018 |
Externally published | Yes |
Keywords
- convolutional neural network
- deep learning
- magnetic resonance imaging
- prostate segmentation
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging