Detection of soft tissue densities from digital breast tomosynthesis: Comparison of conventional and deep learning approaches

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 9785, Issue , 2016, Pages

  Fotin, Sergei V ^a   Yin, Yin ^a   Haldankar, Hrishikesh ^a   Hoffmeister, Jeffrey W ^a   Periaswamy, Senthil ^a  

^a ICAD INC   (United States)

Author keywords

Computer aided detection (CAD); Convolutional neural networks (CNN); Deep learning; Digital breast tomosynthesis (DBT); Digital mammography

Indexed keywords

COMPUTER AIDED INSTRUCTION; CONVOLUTION; DIAGNOSIS; IMAGE SEGMENTATION; ITERATIVE METHODS; MAMMOGRAPHY; MEDICAL IMAGING; NEURAL NETWORKS; TISSUE; TOMOGRAPHY;

COMPUTER-AIDED DETECTION; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; DIGITAL BREAST TOMOSYNTHESIS (DBT); DIGITAL MAMMOGRAPHY;

COMPUTER AIDED DIAGNOSIS;

EID: 84988807853     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.2217045     Document Type: Conference Paper

References (26)

1. Niklason, L. T., Christian, B. T., Niklason, L. E., Kopans, D. B., Castleberry, D. E., Opsahl-Ong, B., Landberg, C. E., Slanetz, P. J., Giardino, A. A., Moore, R., et al., "Digital tomosynthesis in breast imaging, " Radiology 205(2), 399-406 (1997).
2. Sechopoulos, I., "A review of breast tomosynthesis. Part I. The image acquisition process, " Medical physics 40(1), 014301 (2013).
3. Sechopoulos, I., "A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications, " Medical physics 40(1), 014302 (2013).
4. Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., et al., "Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program, " Radiology 267(1), 47-56 (2013).
5. Ciatto, S., Houssami, N., Bernardi, D., Caumo, F., Pellegrini, M., Brunelli, S., Tuttobene, P., Bricolo, P., Fantò, C., et al., "Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study, " The lancet oncology 14(7), 583-589 (2013).
6. Friedewald, S. M., Rafferty, E. A., Rose, S. L., Durand, M. A., Plecha, D. M., Greenberg, J. S., Hayes, M. K., Copit, D. S., Carlson, K. L., Cink, T. M., et al., "Breast cancer screening using tomosynthesis in combination with digital mammography, " JAMA 311(24), 2499-2507 (2014).
7. Reiser, I. and Nishikawa, R., "Computerized Mass Detection for Digital Breast Tomosynthesis, " Recent Advances in Breast Imaging, Mammography, And Computer-Aided Diagnosis of Breast Cancer 155, 409 (2006).
8. Chan, H.-P., Wei, J., Zhang, Y., Helvie, M. A., Moore, R. H., Sahiner, B., Hadjiiski, L., and Kopans, D. B., "Computer-aided detection of masses in digital tomosynthesis mammography: Comparison of three approaches, " Medical physics 35, 4087 (2008).
9. Singh, S., Tourassi, G. D., Baker, J. A., Samei, E., and Lo, J. Y., "Automated breast mass detection in 3D reconstructed tomosynthesis volumes: A featureless approach, " Medical physics 35, 3626 (2008).
10. Palma, G., Muller, S., Bloch, I., and Iordache, R., "Fast detection of convergence areas in digital breast to-mosynthesis, " in [Biomedical Imaging: From Nano to Macro, 2009. ISBI'09. IEEE International Symposium on], 847-850, IEEE (2009).
11. Palma, G., Bloch, I., and Muller, S., "Spiculated lesions and architectural distortions detection in digital breast tomosynthesis datasets, " in [Digital Mammography], 712-719, Springer (2010).
12. Mazurowski, M. A., Lo, J. Y., Harrawood, B. P., and Tourassi, G. D., "Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis, " Journal of biomedical informatics 44(5), 815-823 (2011).
13. van Schie, G., Wallis, M. G., Leiand, K., Danielsson, M., and Karssemeijer, N., "Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms, " Medical physics 40, 041902 (2013).
14. Peters, G., Muller, S., Bernard, S., Iordache, R., Wheeler, F., and Bloch, I., "Reconstruction-independent 3D CAD for calcification detection in digital breast tomosynthesis using fuzzy particles, " in [Progress in Pattern Recognition, Image Analysis and Applications], 400-408, Springer (2005).
15. Park, S. C., Zheng, B., Wang, X.-H., and Gur, D., "Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: An assessment, " in [Medical Imaging], 691507-691507, International Society for Optics and Photonics (2008).
16. Bernard, S., Muller, S., and Onativia, J., "Computer-aided microcalcification detection on digital breast tomosynthesis data: A preliminary evaluation, " in [Digital Mammography], 151-157, Springer (2008).
17. Chan, H.-P., Wu, Y.-T., Sahiner, B., Wei, J., Helvie, M. A., Zhang, Y., Moore, R. H., Kopans, D. B., Hadjiiski, L., and Way, T., "Characterization of masses in digital breast tomosynthesis: Comparison of machine learning in projection views and reconstructed slices, " Medical physics 37, 3576 (2010).
18. Spangler, M. L., Zuley, M. L., Sumkin, J. H., Abrams, G., Ganott, M. A., Hakim, C., Perrin, R., Chough, D. M., Shah, R., and Gur, D., "Detection and classification of calcifications on digital breast tomosynthesis and 2D digital mammography: a comparison, " American Journal of Roentgenology 196(2), 320-324 (2011).
19. Krizhevsky, A., Sutskever, I., and Hinton, G. E., "Imagenet classification with deep convolutional neural networks, " in [Advances in neural information processing systems], 1097-1105 (2012).
20. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D., "Hand-written digit recognition with a back-propagation network, " in [Advances in neural information processing systems], Citeseer (1990).
21. Girshick, R., Donahue, J., Darrell, T., and Malik, J., "Rich feature hierarchies for accurate object detection and semantic segmentation, " in [Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on], 580-587, IEEE (2014).
22. Zhang, W., Doi, K., Giger, M. L., Wu, Y., Nishikawa, R. M., and Schmidt, R. A., "Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network, " Medical Physics 21(4), 517-524 (1994).
23. Sahiner, B., Chan, H.-P., Petrick, N., Wei, D., Helvie, M. A., Adler, D. D., and Goodsitt, M. M., "Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images, " Medical Imaging, IEEE Transactions on 15(5), 598-610 (1996).
24. Lo, S.-C. B., Li, H., Wang, Y., Kinnard, L., and Freedman, M. T., "A multiple circular path convolu-tion neural network system for detection of mammographic masses, " Medical Imaging, IEEE Transactions on 21(2), 150-158 (2002).
25. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T., "Caffe: Convolutional architecture for fast feature embedding, " arXiv preprint arXiv:1408.5093 (2014).
26. D'Orsi, C. J. et al., [ACR BI-RADS Atlas: Breast Imaging Reporting and Data System] (2013).