A Novel Approach for Precise Tissue Tracking in Breast Lumpectomy

One of the most common cancers among women is breast cancer which 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 indicate that the trained network has an accuracy of 0.014 cm with training data, and 0.026 cm with the testing portion of data, demonstrating precision in tumour localization and significantly improving upon current methods. This innovation can potentially enhance patient outcomes by making breast cancer surgery safer, less invasive, and more efficient.