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Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 \u00b1 0.121 per image versus 0.524 \u00b1 0.034 per image), intersection over the union per MC object (0.607 \u00b1 0.250 versus 0.363 \u00b1 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.<\/jats:p>","DOI":"10.1007\/s10278-022-00751-3","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T11:03:06Z","timestamp":1677150186000},"page":"1016-1028","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach"],"prefix":"10.1007","volume":"36","author":[{"given":"Chrysostomos","family":"Marasinou","sequence":"first","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jeremy","family":"Paige","sequence":"additional","affiliation":[]},{"given":"Akinyinka","family":"Omigbodun","sequence":"additional","affiliation":[]},{"given":"Noor","family":"Nakhaei","sequence":"additional","affiliation":[]},{"given":"Anne","family":"Hoyt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5168-070X","authenticated-orcid":false,"given":"William","family":"Hsu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"6","key":"751_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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William Hsu was a recipient of a research grant from Siemens Medical Solutions unrelated to this work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}