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Neural Computing and Applications

, Volume 31, Issue 4, pp 1069–1081 | Cite as

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

  • Xin Wang
  • Yi GuoEmail author
  • Yuanyuan WangEmail author
  • Jinhua Yu
Original Article

Abstract

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

Keywords

Automated breast volume scanner Breast tumor Computer-aided detection Convolutional neural network Superpixel 

Notes

Acknowledgement

This work is supported by the National Basic Research Program of China (2015CB755500) and the National Natural Science Foundation of China (61271071, 61401102, 81627804).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Siegel RL, Miller KD, Jemal A (2016) Cancer statistics 2016. CA Cancer J Clin 66(1):7–30CrossRefGoogle Scholar
  2. 2.
    Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90CrossRefGoogle Scholar
  3. 3.
    Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Bohm-Velez M, Pisano ED, Jong RA, Evans WP, Morton MJ, Mahoney MC, Larsen LH, Barr RG, Farria DM, Marques HS, Boparai K (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163CrossRefGoogle Scholar
  4. 4.
    Moon WK, Shen YW, Min SB, Huang CS, Chen JH, Chang RF (2012) Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Trans Med Imaging 32(7):1191–1200CrossRefGoogle Scholar
  5. 5.
    Lo CM, Chen RT, Chang YC, Yang YW, Hung MJ, Huang CS, Chang RF (2014) Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. IEEE Trans Med Imaging 33(7):1503–1511CrossRefGoogle Scholar
  6. 6.
    Tan T, Platel B, Mus R, Tabar L, Mann RM, Karssemeijer N (2013) Computer-aided detection of cancer in automated 3-D breast ultrasound. IEEE Trans Med Imaging 32(9):1698–1706CrossRefGoogle Scholar
  7. 7.
    Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In proceedings MICCAI, 2013, pp 411–418Google Scholar
  8. 8.
    Li Q, Cai W, Wang X, Zhou Y, Feng DD and Chen M (2014) Medical image classification with convolutional neural network. In proceedings ICARCV, 2014, pp 844–848Google Scholar
  9. 9.
    Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In proceedings MICCAI, 2013, pp 246–253Google Scholar
  10. 10.
    Roth H, Yao J, Lu L, Stieger J, Burns J and Summers RM (2015) Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications. Lecture notes in computational vision and biomechanics, vol 20(1), pp 3–12Google Scholar
  11. 11.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, SüSstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11):2274–2282CrossRefGoogle Scholar
  12. 12.
    Chu J, Min H, Liu L, Lu W (2015) A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation. Med Phys 42(7):3859–3869CrossRefGoogle Scholar
  13. 13.
    Zhou M, Wu Z, Chen D, Zhou Y (2013) An improved vein image segmentation algorithm based on SLIC and Niblack threshold method. In proceedings SPIE9045, pp 90450D-90450D-10Google Scholar
  14. 14.
    Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in CT imaging. In SPIE Proceedings Medical Imaging 2015: Image Processing 9413(9): 476-484Google Scholar
  15. 15.
    Wang X, Guo Y, Wang Y (2015) Automatic detection of the region of interest in breast ultrasound images based on local phase information. Bio-Med Mater Eng 26(s1):S1265–S1273CrossRefGoogle Scholar
  16. 16.
    Dosil R, Pardo XM, Fernandez-Vidal XR (2006) Data driven synthesis of composite feature detectors for 3D image analysis. Image Vis Comput 24(3):225–238CrossRefGoogle Scholar
  17. 17.
    Shan J, Cheng HD, Wang Y (2012) A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering. Med Phys 39(9):5669–5682CrossRefGoogle Scholar
  18. 18.
    Shan J, Cheng HD, Wang Y (2012) Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol 38(2):262–275CrossRefGoogle Scholar
  19. 19.
    Roth H, Lu L, Liu J, Yao J, Seff A, Cherry K, Kim L, Summers R (2016) Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35(5):1170–1181CrossRefGoogle Scholar
  20. 20.
    Vedaldi A, Lenc K (2016) MatConvNet-convolutional neural networks for MATLAB. http://www.vlfeat.org/matconvnet/ Jan
  21. 21.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In proceedings neural information and processing systemsGoogle Scholar
  22. 22.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI 24(7):971–987CrossRefzbMATHGoogle Scholar
  23. 23.
    Kovesi P (2000) Phase congruency: a low-level image invariant. Psychol Res 64:136–148CrossRefGoogle Scholar
  24. 24.
    Udupa JK, LaBlanc VR, Schmidt H, Imielinska C, Saha PK, Grevera GJ, Zhuge Y, Currie LM, Molholt P, Jin Y (2002) A methodology for evaluating image-segmentation algorithms. In proceedings spie medical imaging, pp 266–277Google Scholar
  25. 25.
    Chakraborty DP (1989) Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data. Med Phys 16:561–568CrossRefGoogle Scholar
  26. 26.
    Chakraborty DP, Breatnach ES, Yester MV, Soto B, Barnes GT, Fraser RG (1986) Digital and conventional chest imaging: a modified ROC study of observer performance using simulated nodules. Radiology 158(1):35–39CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina
  2. 2.Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiShanghaiChina

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