Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns
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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.
KeywordsAutomated breast volume scanner Breast tumor Computer-aided detection Convolutional neural network Superpixel
This work is supported by the National Basic Research Program of China (2015CB755500) and the National Natural Science Foundation of China (61271071, 61401102, 81627804).
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Conflict of interest
The authors declare that they have no conflict of interest.
- 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
- 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.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.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.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
- 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.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
- 20.Vedaldi A, Lenc K (2016) MatConvNet-convolutional neural networks for MATLAB. http://www.vlfeat.org/matconvnet/ Jan
- 21.Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In proceedings neural information and processing systemsGoogle Scholar
- 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