Bellezza-Degrado

L'articolo analizza le conseguenze giuridiche ed estetiche dell'introduzione dell'imprevedibilità degli eventi nel paradigma tradizionale dell'ordine. La forma in questo contesto mira non già alla normalizzazione dei fenomeni, ma alla capacità decisiva di corrispondere a eventi imprevedibili. Si abbozza quindi un Manifesto della bellezza civile come nuova sintesi tra l'universalità della legge e la singolarità eccezionale dell'esperienza

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668S123132195Kuhl, C. K. (2015). The Changing World of Breast Cancer. 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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine, 177, 123-132. doi:10.1016/j.cmpb.2019.05.022Ciatto, S., Houssami, N., Apruzzese, A., Bassetti, E., Brancato, B., Carozzi, F., … Scorsolini, A. (2005). Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. The Breast, 14(4), 269-275. doi:10.1016/j.breast.2004.12.004Skaane, P. (2009). Studies comparing screen-film mammography and full-field digital mammography in breast cancer screening: Updated review. Acta Radiologica, 50(1), 3-14. doi:10.1080/02841850802563269Van der Waal, D., den Heeten, G. J., Pijnappel, R. M., Schuur, K. H., Timmers, J. M. H., Verbeek, A. L. M., & Broeders, M. J. M. (2015). Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting. PLOS ONE, 10(9), e0136667. doi:10.1371/journal.pone.0136667Kim, S. H., Lee, E. H., Jun, J. K., Kim, Y. M., Chang, Y.-W., … Lee, J. H. (2019). Interpretive Performance and Inter-Observer Agreement on Digital Mammography Test Sets. Korean Journal of Radiology, 20(2), 218. doi:10.3348/kjr.2018.0193Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246. doi:10.1093/bib/bbx044LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., … Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. 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Ecology, 26(3), 297-302. doi:10.2307/1932409Pollán, M., Llobet, R., Miranda-García, J., Antón, J., Casals, M., Martínez, I., … Salas-Trejo, D. (2013). Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-242Llobet, R., Pollán, M., Antón, J., Miranda-García, J., Casals, M., Martínez, I., … Pérez-Cortés, J.-C. (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine, 116(2), 105-115. doi:10.1016/j.cmpb.2014.01.021He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., & Chao, Y. (2017). The connected-component labeling problem: A review of state-of-the-art algorithms. Pattern Recognition, 70, 25-43. doi:10.1016/j.patcog.2017.04.018Wu, K., Otoo, E., & Suzuki, K. (2008). Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 12(2), 117-135. doi:10.1007/s10044-008-0109-yShen, R., Yan, K., Xiao, F., Chang, J., Jiang, C., & Zhou, K. (2018). Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection. Journal of Digital Imaging, 31(5), 680-691. doi:10.1007/s10278-018-0068-9Yin, K., Yan, S., Song, C., & Zheng, B. (2018). A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. International Journal of Computer Assisted Radiology and Surgery, 14(2), 237-248. doi:10.1007/s11548-018-1867-7James, J. . (2004). The current status of digital mammography. Clinical Radiology, 59(1), 1-10. doi:10.1016/j.crad.2003.08.011Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Jain, A. K. (2010). 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