A deep learning health data analysis approach: automatic 3D prostate MR segmentation with densely-connected volumetric ConvNets

Qikui Zhu, Bo Du, Jia Wu, Pingkun Yan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

16 Citations (Scopus)


Automated prostate segmentation in 3D medical images play an important role in many clinical applications, such as diagnosis of prostatitis, prostate cancer and enlarged prostate. However, it is still a challenging task due to the complex background, lacking of clear boundary and various shape and texture between the slices. In this paper, we propose a novel 3D convolutional neural network with densely-connected layers to automatically segment the prostate from Magnetic Resonance(MR) images. Compared with other methods, our method has three compelling advantages. First, our model can effectively detect the prostate region in a volume-to-volume manner by utilizing the 3D convolution rather than the 3D convolution, which can fully exploit both spatial and region information. Second, the proposed network architecture alleviates the vanishing-gradient problem, strengthens the information propagation between layers, overcomes the problem of over-fitting and makes the network deeper by adopting a densely-connected manner. Third, besides the densely-connected manner inside each block, we also adopt the long connections strategy between blocks. We evaluate our proposed model on prostate dataset. The experimental results show that our model achieved significant segmentation results and outperformed other state-of-arts methods.
Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
Publication statusPublished - 1 Jul 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018


Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CityRio de Janeiro


  • biological organs
  • biomedical MRI
  • cancer
  • convolution
  • data analysis
  • feedforward neural nets
  • image segmentation
  • learning (artificial intelligence)
  • medical image processing
  • neural net architecture
  • long connections strategy
  • prostate dataset
  • deep learning health data analysis approach
  • densely-connected volumetric ConvNets
  • automated prostate segmentation
  • 3D medical images
  • clinical applications
  • prostate cancer
  • enlarged prostate
  • densely-connected layers
  • prostate region
  • spatial region information
  • network architecture
  • vanishing-gradient problem
  • 3D convolutional neural network
  • magnetic resonance images
  • automatic 3D prostate MR segmentation
  • prostatitis
  • Image segmentation
  • Three-dimensional displays
  • Convolution
  • Encoding
  • Feature extraction
  • Decoding
  • Solid modeling


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