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 (ESMDA),
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.

See our lab website for more details and a copy of the paper: http://www.ece.ualberta.ca/~tbs (go to Publications).