Breast conserving surgery followed by radiotherapy is the standard of care for early-stage breast cancer patients. Deformable image registration (DIR) can in principle be of great value for accurate localization of the original tumor site to optimize breast irradiation after surgery. However, current state-of-the-art DIR methods are not very successful when tissue is present in one image but not in the other (i.e., in case of content mismatch). To tackle this challenge, we combined a multi-objective DIR approach with simulated tissue removal. Parameters defining the area to be removed as well as key DIR parameters (that are often tuned manually for each DIR case) are determined by a multi-objective optimization process. In multi-objective optimization, not one, but a set of solutions is found, that represent high-quality trade-offs between objectives of interest. We used three state-of-the-art multi-objective evolutionary algorithms as meta-optimizers to search for the optimal parameters, and tested our approach on four test cases of computed tomography (CT) images of breast cancer patients before and after surgery. Results show that using meta-optimization with simulated tissue removal improves the performance of DIR. This way, sets of high-quality solutions could be obtained with a mean target registration error of 2.4 mm over four test cases and an estimated excised volume that is within 20% from the measured volume of the surgical resection specimen.

Additional Metadata
Keywords Breast CT, Deformable image registration, Evolutionary algorithms, Multi-objective optimization
Persistent URL dx.doi.org/10.1117/12.2512760
Series Proceedings of SPIE
Conference SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications
Citation
Pirpinia, K, Bosman, P.A.N, Sonke, J.-J, van Herk, M, & Alderliesten, T. (2019). Evolutionary multi-objective meta-optimization of deformation and tissue removal parameters improves the performance of deformable image registration of pre- and post-surgery images. In Medical Imaging 2019: Image Processing. doi:10.1117/12.2512760