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13 March 2019 Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment
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Abstract
While breast cancer screening recommendations vary by agency, all agencies recommend mammographic screening with some frequency over some portion of a woman’s lifetime. Temporal evaluation of these images may inform personalized risk of breast cancer. However, due to the highly deformable nature of breast tissue, the positioning of breast tissue may vary widely between exams. Therefore, registration of physical regions in the breast over time points is a critical first step in computerized analysis of changes in breast parenchyma over time. While a postregistration image is altered and therefore not appropriate for radiomic texture analysis, the registration process produces a mapping of points which may aid in aligning similar image regions across multiple time points. In this study, a total of 633 mammograms from 87 patients were retrospectively collected. These images were sorted into 1144 temporal pairs, where each combination of images of a given women of a given laterality was used to form a temporal pair. B-splines registration and multi-resolution registration were performed on each mammogram pair. While the B-splines took an average of 552.8 CPU seconds per registration, multi-resolution registration took only an average of 346.2 CPU seconds per registration. Multi-resolution registration had a 15% lower mean square error, which was significantly different than that of B-splines (p<0.001). While previous work aimed to allow radiologists to visually evaluate the registered images, this study identifies corresponding points on images for use in assessing interval change for risk assessment and early detection of cancer through deep learning and radiomics.
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Kayla Mendel, Hui Li, Nabihah Tayob, Randa El-Zein, Isabelle Bedrosian, and Maryellen Giger "Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095041 (13 March 2019); https://doi.org/10.1117/12.2512792
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