2 research outputs found
Parameters identification method for breast biomechanical numerical model
Bio-mechanical breast simulations are based on a gravity free geometry as a
reference domain and a nonlinear mechanical model parameterised by physical
coefficients. As opposed to complex models proposed in the literature based on
medical imagery, we propose a simple but yet realistic model that uses a basic
set of measurements easy to realise in the context of routinely operations.
Both the mechanical system and the geometry are controlled with parameters we
shall identify in an optimisation procedure. We give a detailed presentation of
the model together with the optimisation method and the associated
discretisation. Sensitivity analysis is then carried out to evaluate the
robustness of the method.Comment: 22 pages including references, 7 figures, 8 tables. Paper submitted
to Journal of Medical & Biological Engineering & Computing and waiting for an
answe
An Hybrid Method for the Estimation of the Breast Mechanical Parameters
There are several numerical models that describe real phenomena being used to
solve complex problems. For example, an accurate numerical breast model can
provide assistance to surgeons with visual information of the breast as a
result of a surgery simulation. The process of finding the model parameters
requires numeric inputs, either based in medical imaging techniques, or other
measures. Inputs can be processed by iterative methods (inverse elasticity
solvers). Such solvers are highly robust and provide solutions within the
required degree of accuracy. However, their computational complexity is costly.
On the other hand, machine learning based approaches provide outputs in
real-time. Although high accuracy rates can be achieved, these methods are not
exempt from producing solutions outside the required degree of accuracy. In the
context of real life situations, a non accurate solution might present
complications to the patient.
We present an hybrid parameter estimation method to take advantage of the
positive features of each of the aforementioned approaches. Our method
preserves both the real-time performance of deep-learning methods, and the
reliability of inverse elasticity solvers. The underlying reasoning behind our
proposal is the fact that deep-learning methods, such as neural networks, can
provide accurate results in the majority of cases and they just need a
fail-safe system to ensure its reliability. Hence, we propose using a
Multilayer Neural Networks (MNN) to get an estimation which is in turn
validated by a iterative solver. In case the MNN provides an estimation not
within the required accuracy range, the solver refines the estimation until the
required accuracy is achieved. Based on our results we can conclude that the
presented hybrid method is able to complement the computational performance of
MNNs with the robustness of iterative solver approaches.Comment: 8 pages, 5 figure