Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

Valentina Candiani,Matteo Santacesaria

doi:10.3934/mine.2022029

Valentina Candiani, Matteo Santacesaria

Open Access

https://doi.org/10.3934/mine.2022029

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Abstract

<abstract><p>We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with $ 40\, 000 $ samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve $ \geq 90\% $ average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy $ \geq 80\% $. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.</p></abstract>

Highlights

Electrical impedance tomography (EIT) is a noninvasive imaging modality for recovering information about the electrical conductivity inside a physical body from boundary measurements of current and potential
It can clearly be seen that the overall accuracy is significantly lower than in the case of fully connected neural network (FCNN)
This is arguably due to the fact that our convolutional neural network (CNN) tend to overfit the data, resulting in a high accuracy for the training set, which significantly degrades in the other test cases

Summary

Introduction

Electrical impedance tomography (EIT) is a noninvasive imaging modality for recovering information about the electrical conductivity inside a physical body from boundary measurements of current and potential. The reconstruction process of EIT requires the solution of a highly nonlinear inverse problem on noisy data. This problem is typically ill-conditioned [1,2,3] and solution algorithms need either simplifying assumptions or regularization strategies based on a priori knowledge. Machine learning has arisen as a data-driven alternative that has shown tremendous improvements upon the ill-posedness of several inverse problems [4,5,6]. It has been already successfully applied in EIT imaging [7,8,9,10]. The purpose of this work is to apply machine learning to the problem of classification of brain strokes from EIT data

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