Automated segmentation and feature extraction in cardiac electrical impedance tomography images

Saaid H. Arshad; Ethan K. Murphy; Ryan J. Halter

doi:10.1117/12.2293917

12 March 2018 Automated segmentation and feature extraction in cardiac electrical impedance tomography images

Saaid H. Arshad, Ethan K. Murphy, Ryan J. Halter

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781Q (2018) https://doi.org/10.1117/12.2293917
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

A non-invasive and accurate modality that can continuously monitor stroke volume (SV) for extended periods of time is desired to allow for more proactive care of an increasing population of patients living with heart failure. Electrical impedance tomography (EIT) has been proposed as a method for accurate, non-invasive, continuous, and long-term SV monitoring. While cardiac EIT has been explored, clinical translation has yet to occur and a standardized method for evaluation and comparison of cardiac EIT images is desired. This work explores an automated process for segmenting and extracting features from the images that allow for evaluation and comparison. A simulation study was conducted using the 4D XCAT model to evaluate the proposed method’s ability to automatically segment and extract features from images reconstructed at various phases of the cardiac cycle. The same procedure was then applied to EIT reconstructions on data collected from five healthy volunteers. The automated segmentation is able to accurately capture the heart region-of-interest (ROI) in various images and extract features, which allows comparison of desired signals across reconstructions. ROI mean conductivity, ROI area, sum of conductivities within the ROI, and ROI maximum conductivity were chosen as promising features from the simulation study, with R² values of 0.61, 0.73, 0.75, and 0.66 for a single heart-cycle, and minimum SV distinguishability of 25.54, 12.16, 12.16, and 17.22 ml. In experimental data, the area feature showed the least variation across individual reconstructions while the sum feature showed the highest variation.

Citation Download Citation

Saaid H. Arshad, Ethan K. Murphy, and Ryan J. Halter "Automated segmentation and feature extraction in cardiac electrical impedance tomography images", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781Q (12 March 2018); https://doi.org/10.1117/12.2293917

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