Classification of stroke using neural networks in electrical impedance tomography

Autor: Rashmi Murthy, Matti Lassas, Juan Pablo Agnelli, Samuli Siltanen, Matteo Santacesaria, Aynur Çöl
Přispěvatelé: Department of Mathematics and Statistics, Inverse Problems, Matti Lassas / Principal Investigator, Doctoral Programme in Mathematics and Statistics, Mikko Samuli Siltanen / Principal Investigator
Rok vydání: 2020
Předmět:
02 engineering and technology
Iterative reconstruction
01 natural sciences
Edge detection
Theoretical Computer Science
Mathematics - Analysis of PDEs
FOS: Electrical engineering
electronic engineering
information engineering
FOS: Mathematics
111 Mathematics
0202 electrical engineering
electronic engineering
information engineering
Medical imaging
Sensitivity (control systems)
Classification
EIT
Inverse problems
Neural networks
VHED function
BRAIN
0101 mathematics
Electrical impedance tomography
Mathematical Physics
Mathematics
Modality (human–computer interaction)
Artificial neural network
inverse problems
business.industry
D-BAR METHOD
Applied Mathematics
Image and Video Processing (eess.IV)
020206 networking & telecommunications
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Inverse problem
neural networks
Computer Science Applications
010101 applied mathematics
UNIQUENESS
classification
Signal Processing
Artificial intelligence
business
Analysis of PDEs (math.AP)
Zdroj: Inverse Problems. 36:115008
ISSN: 1361-6420
0266-5611
DOI: 10.1088/1361-6420/abbdcd
Popis: Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).
Databáze: OpenAIRE
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