Abstract
Mammography is the first-line modality for screening and diagnosis of breast cancer. Following the common practice of radiologists to examine two mammography views, we propose a fully automated dual-view analysis framework for breast mass detection in mammograms. The framework combines unsupervised segmentation and random-forest classification to detect and rank candidate masses in cranial-caudal (CC) and mediolateral-oblique (MLO) views. Subsequently, it estimates correspondences between pairs of candidates in the two views. The performance of the method was evaluated using a publicly available full-field digital mammography database (INbreast). Dual-view analysis provided area under the ROC curve of 0.94, with detection sensitivity of 87% at specificity of 90%, which significantly improved single-view performance (72% sensitivity at 90% specificity, 78% specificity at 87% sensitivity, P<0.05). One-to-one mapping of candidate masses from two views facilitated correct estimation of the breast quadrant in 77% of the cases. The proposed method may assist radiologists to efficiently identify and classify breast masses.
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Mammography Quality Standards Act’s National Statistics. http://www.fda.gov/Radiation-EmittingProducts/MammographyQualityStandardsActandProgram/
Oliver, A., Freixenet, J., MartÃ, J., Pérez, E., Pont, J., Denton, E.R.E., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14, 87–110 (2010)
Van Engeland, S., Timp, S., Karssemeijer, N.: Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views. Med. Phys. 33, 3203–3212 (2006)
Yuan, Y., Giger, M.L., Li, H., Sennett, C.: Correlative feature analysis on FFDM. Med. Phys. 35, 5490–5500 (2008)
Zheng, B., Leader, J.K., Abrams, G.S., Lu, A.H., Wallace, L.P., Maitz, G.S., Gur, D.: Multiview-based computer-aided detection scheme for breast masses. Med. Phys. 33, 3135–3143 (2006)
Wiemker, R., Kutra, D., Heese, H., Buelow, T.: Identification of corresponding lesions in multiple mammographic views using star-shaped iso-contours. In: Aylward, S., Hadjiiski, L.M. (eds.) SPIE Medical Imaging, p. 90351A. International Society for Optics and Photonics (2014)
Paquerault, S., Petrick, N., Chan, H.-P., Sahiner, B., Helvie, M.A.: Improvement of computerized mass detection on mammograms: fusion of two-view information. Med. Phys. 29, 238–247 (2002)
Velikova, M., Samulski, M., Lucas, P.J.F., Karssemeijer, N.: Improved mammographic CAD performance using multi-view information: a Bayesian network framework. Phys. Med. Biol. 54, 1131–1147 (2009)
Li, H., Giger, M.L., Yuan, Y., Chen, W., Horsch, K., Lan, L., Jamieson, A.R., Sennett, C.A., Jansen, S.A.: Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad. Radiol. 15, 1437–1445 (2008)
Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: Toward a Full-field Digital Mammographic Database. Acad. Radiol. 19, 236–248 (2012)
Zlotnick, A., Ophir, B., Kisilev, P.: Hybrid Unsupervised-Supervised Lesion Detection in Mammograms. SPIE Medical Imaging (2015)
Zlotnick, A., Lozinskii, E.: Semantic thresholding. Pattern Recognit. Lett. 5, 321–328 (1987)
Timp, S., Karssemeijer, N.: A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med. Phys. 31, 958–971 (2004)
Rojas DomÃnguez, A., Nandi, A.K.: Improved dynamic-programming-based algorithms for segmentation of masses in mammograms. Med. Phys. 34, 4256–4269 (2007)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn (2006)
Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)
Kisilev, P., Freedman, D., Wallach, E., Tzadok, A., Naveh, Y.: DFlow and DField: New features for capturing object and image relationships. In: 21st International Conference on Pattern Recognition (ICPR), pp. 3590–3593. IEEE (2012)
Wei, J., Sahiner, B., Hadjiiski, L.M., Chan, H.-P., Petrick, N., Helvie, M.A., Roubidoux, M.A., Ge, J., Zhou, C.: Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 32, 2827–2838 (2005)
Kozegar, E., Soryani, M., Minaei, B., Domingues, I.: Assessment of a novel mass detection algorithm in mammograms. J. Cancer Res. Ther. 9, 592–600 (2013)
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Amit, G., Hashoul, S., Kisilev, P., Ophir, B., Walach, E., Zlotnick, A. (2015). Automatic Dual-View Mass Detection in Full-Field Digital Mammograms. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_6
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DOI: https://doi.org/10.1007/978-3-319-24571-3_6
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