An Hybrid Method for the Estimation of the Breast Mechanical Parameters

by   Diogo Lopes, et al.

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.


page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8


A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods

Iterative solvers of linear systems are a key component for the numerica...

Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality

Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization ...

Power Bundle Adjustment for Large-Scale 3D Reconstruction

We present the design and the implementation of a new expansion type alg...

One Network, Many Robots: Generative Graphical Inverse Kinematics

Quickly and reliably finding accurate inverse kinematics (IK) solutions ...

Parameters identification method for breast biomechanical numerical model

Bio-mechanical breast simulations are based on a gravity free geometry a...

Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows

It is tested whether machine learning methods can be used for preconditi...

Please sign up or login with your details

Forgot password? Click here to reset