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SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms
Medical Physics ( IF 3.8 ) Pub Date : 2021-07-30 , DOI: 10.1002/mp.15017
Xiaoyuan Guo 1 , W Charles O'Neill 2 , Brianna Vey 3 , Tianen Christopher Yang 2 , Thomas J Kim 4 , Maryzeh Ghassemi 5 , Ian Pan 6 , Judy Wawira Gichoya 3, 7 , Hari Trivedi 3, 7 , Imon Banerjee 3, 7
Affiliation  

Measurements of breast arterial calcifications (BAC) can offer a personalized, non-invasive approach to risk-stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications.

中文翻译:

SCU-Net:一种用于在乳房 X 光照片上分割和量化乳腺动脉钙化的深度学习方法

乳腺动脉钙化 (BAC) 的测量可以提供一种个性化的、非侵入性的方法来对女性进行心血管疾病(如心脏病发作和中风)的风险分层。我们的目标是准确地检测和分割乳房 X 光照片中的乳腺动脉钙化,并提出新的测量方法来量化检测到的 BAC,以用于未来的临床应用。
更新日期:2021-07-30
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