Advertisement

Boundary Regularized Convolutional Neural Network for Layer Parsing of Breast Anatomy in Automated Whole Breast Ultrasound

  • Cheng Bian
  • Ran Lee
  • Yi-Hong Chou
  • Jie-Zhi ChengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

A boundary regularized deep convolutional encoder-decoder network (ConvEDNet) is developed in this study to address the difficult anatomical layer parsing problem in the noisy Automated Whole Breast Ultrasound (AWBUS) images. To achieve better network initialization, a two-stage adaptive domain transfer (2DT) is employed to land the VGG-16 encoder on the AWBUS domain with the bridge of network training for AWBUS edge detector. The knowledge transferred encoder is denoted as VGG-USEdge. To further augment the training of ConvEDNet, a deep boundary supervision (DBS) strategy is introduced to regularize the feature learning for better robustness to speckle noise and shadowing effect. We argue that simply counting on the image context cue, which can be learnt with the guidance of label maps, may not be sufficient to deal with the intrinsic noisy property of ultrasound images. With the regularization of boundary cue, the segmentation learning can be boosted. The efficacy of the proposed 2DT-DBS ConvEDNet is corroborated with the extensive comparison to the state-of-the-art deep learning segmentation methods. The segmentation results may assist the clinical image reading, particularly for junior medical doctors and residents and help to reduce false-positive findings from a computer-aided detection scheme.

Keywords

Segmentation Ultrasound Breast Deep learning 

Notes

Acknowledgement

This work was supported by the National Natural Science Funds of China (No. 61501305), the Shenzhen Basic Research Project (No. JCYJ20150525092940982), the Natural Science Foundation of SZU (No. 2016089).

References

  1. 1.
    McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomark. Prev. 15, 1159–1169 (2006)CrossRefGoogle Scholar
  2. 2.
    Tan, T., et al.: Chest wall segmentation in automated 3D breast ultrasound scans. Med. Image Anal. 17, 1273–1281 (2013)CrossRefGoogle Scholar
  3. 3.
    Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR 2015, pp. 3431–3440 (2015)Google Scholar
  4. 4.
    Noh, H., et al.: Learning deconvolution network for semantic segmentation. In: ICCV 2015, pp. 1520–1528 (2015)Google Scholar
  5. 5.
    Badrinarayanan, V.: Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv preprint arXiv:1505.07293 (2015)
  6. 6.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  7. 7.
    Lee, C.-Y., et al.: Deeply-Supervised nets. In: AISTATS, 2015, June 2015Google Scholar
  8. 8.
    Chen, H., et al.: DCAN: deep contour-aware networks for accurate gland segmentation. In: CVPR 2016, pp. 2487–2496 (2016)Google Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  10. 10.
    Shin, H.-C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE TMI 35, 1285–1298 (2016)Google Scholar
  11. 11.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV 2015, pp. 1395–1403 (2015)Google Scholar
  12. 12.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM MM 2014, pp. 675–678 (2014)Google Scholar
  13. 13.
    Gubern-Merida, A., et al.: Breast segmentation and density estimation in breast MRI: A fully automatic framework. IEEE JBHI 19, 349–357 (2015)Google Scholar
  14. 14.
    Huang, Q., et al.: Breast ultrasound image segmentation: a survey. IJCARS 12, 1–5 (2017)Google Scholar
  15. 15.
    Cheng, J.-Z., et al.: ACCOMP: augmented cell competition algorithm for breast lesion demarcation in sonography. Med. Phys. 37(12), 6240–6252 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cheng Bian
    • 1
  • Ran Lee
    • 1
  • Yi-Hong Chou
    • 2
  • Jie-Zhi Cheng
    • 3
    Email author
  1. 1.School of Biomedical EngineeringShenzhen UniversityShenzhenChina
  2. 2.Department of RadiologyTaipei Veterans General HospitalTaipeiTaiwan
  3. 3.Department of Electrical EngineeringChang Gung UniversityTaoyuanTaiwan

Personalised recommendations