Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Nov;13(11):1687-1696.
doi: 10.1007/s11548-018-1841-4. Epub 2018 Aug 7.

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Minh Nguyen Nhat To  1 , Dang Quoc Vu  1 , Baris Turkbey  2 , Peter L Choyke  2 , Jin Tae Kwak  3
Affiliations

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Minh Nguyen Nhat To et al. Int J Comput Assist Radiol Surg. 2018 Nov.

Abstract

Purpose: We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

Methods: A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.

Results: Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.

Conclusions: The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.

Keywords: Deep learning; Dense connections; Grouped convolution; Magnetic resonance imaging; Prostate segmentation.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
The framework of the proposed deep convolutional neural network (CNN). There are 7 hidden layers in the CNN. The number shows the feature or channel dimension of each hidden layer.
Fig. 2.
Fig. 2.
Filters and output of the first hidden layer of the CNN. Left: Filters of the first hidden layer (3×3 filters). Right: Outputs of the first hidden layer (first 36 only).
Fig. 3.
Fig. 3.
Output of the fourth layer (left) and the fifth layer (right).
Fig. 4.
Fig. 4.
The qualitative results of the proposed method. The red curves represent the prostate contours obtained by the proposed method, while the blue curves represent the contours obtained from manual segmentation by an experienced radiologist.
See this image and copyright information in PMC

References

    1. Siegel RL, Miller KD, and Jemal A, “Cancer statistics,”. CA: a cancer journal for clinicians, 66(1): p. 7–30, (2016). - PubMed
    1. Fei B, Kemper C, and Wilson DL, “A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes.” Comput Med Imaging Graph, 27(4): p. 267–81, (2003). - PubMed
    1. Fei B, et al., “Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer.” IEEE Trans Med Imaging, 22(4): p. 515–525, (2003). - PubMed
    1. Wu Qiu JY, Ukwatta Eranga, Sun Yue, Rajchl Martin, and Fenster Aaron, “Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images.” IEEE Transactions on Medical Imaging, 33(4): p. 947–960, (2014). - PubMed
    1. Litjens G, et al., “Computer-aided detection of prostate cancer in MRI.” IEEE transactions on Medical Imaging, 33(5): p. 1083–1092, (2014). - PubMed