Abstract
The development of a three-dimensional (3D) planing tool for breast cancer surgery requires the existence of proper deformable models of the breast, with parameters that can be manipulated to obtain the desired shape. However, modelling breast is a challenging task due to the lack of physical landmarks that remain unchanged after deformation. In this paper, the fitting of a 3D point cloud of the breast to a parametric model suitable for surgery planning is investigated. Regression techniques were used to learn breast deformation functions from exemplar data, resulting in comprehensive models easy to manipulate by surgeons. New breast shapes are modelled by varying the type and degree of deformation of three common deformations: ptosis, turn and top-shape.
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Acknowledgements
This work was funded by the Project NanoSTIMA: Macro to Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016 financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundao para a Cincia e a Tecnologia (FCT) within PhD grant number SFRH/BD/115616/2016.
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Bessa, S., Zolfagharnasab, H., Pereira, E., Oliveira, H.P. (2017). Prediction of Breast Deformities: A Step Forward for Planning Aesthetic Results After Breast Surgery. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_30
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DOI: https://doi.org/10.1007/978-3-319-58838-4_30
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