Abstract
Digital Breast Tomosynthesis (DBT) images are widely used to increase breast cancer detection and reduce recall rates in healthcare systems for breast cancer detection. In the field of medical imaging, computer-aided diagnosis (CAD) systems are used to analyze this type of images. Generally, in order to achieve an early detection of breast cancer, these CAD systems start with the reconstruction part of the image, the pre-processing step and then the segmentation and classification. However, the post-acquisition techniques of DBT can impact the detection and diagnosis of breast cancer, and bias the final decision in computer-aided detection and diagnosis systems. Mainly, the reconstruction phase in computer aided detection systems, that helps prepare the DBT for further analysis, such as segmentation and classification of abnormalities. In this paper, we present a survey of different techniques for DBT reconstruction, that we compared theoretically in terms of advantages and drawbacks, particularly for healthcare systems dedicated to breast cancer detection.
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Samiry, I., Ait Lbachir, I., Daoudi, I., Tallal, S., Adil, S. (2023). Digital Breast Tomosynthesis Reconstruction Techniques in Healthcare Systems: A Review. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_17
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