A Novel Approach For Precise Tissue Tracking In Breast Lumpectomy

Breast cancer is one of the most common cancers in the female population and can be treated surgically in the early stages with a lumpectomy technique. In the context of breast lumpectomy procedures, accurately tracking tumours presents a critical challenge worsened by various sources of anatomical deformations, including breathing, tissue cutting, and ultrasound probe pressure. To address this, we explore how a realistic tissue deformation simulator can enhance the precision of locating internal targets by accurately assessing the deformation applied to a preoperative model of the breast, considering the distinct mechanical properties of both the breast tissue and the tumour within it. Our method uses advanced artificial intelligence techniques by combining a generative variation autoencoder (GNN-VAE) and an updating method called ensemble smoother with multiple data assimilation (ES-MDA), creating a dynamic model based exclusively on surface node data to update all nodes within the tissue. By leveraging a realistic tissue deformation simulator, our approach uses breast surface tracking to infer full tissue deformations. This makes the method compatible with various simulation tools and suitable for tissues with complex properties. The results demonstrate that the accuracy of the trained network on training data is 0.014 cm, and on testing data is 0.026 cm which shows precision in tumour localization, significantly improving upon current methods. This innovation has the potential to enhance patient outcomes by making breast cancer surgery safer, less invasive, and more efficient.