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Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach.

Methods

The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result.

Results

We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net.

Conclusion

Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.

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Notes

  1. https://github.com/superxuang/caffe_3d_crf_rnn.

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Authors and Affiliations

  1. Image Processing Center, School of Astronautics, Beihang University, Xueyuan Road No.37, Beijing, China

    Xuanang Xu, Fugen Zhou & Bo Liu

  2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China

    Fugen Zhou & Bo Liu

Authors
  1. Xuanang Xu
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  2. Fugen Zhou
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  3. Bo Liu
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Correspondence to Bo Liu.

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Funding

This work is funded by the National Key R&D Program of China (2017YFC0113100) and the National Natural Science Foundation of China (61601012).

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The authors declare that they have no conflict of interest.

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For this type of study formal consent is not required.

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Statement of informed consent is not applicable since the manuscript does not contain any participants’ data.

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Xu, X., Zhou, F. & Liu, B. Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN. Int J CARS 13, 967–975 (2018). https://doi.org/10.1007/s11548-018-1733-7

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  • DOI: https://doi.org/10.1007/s11548-018-1733-7

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