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Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT

Citation

KO, Yen Fen (2021), Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT , Dryad, Dataset, https://doi.org/10.5061/dryad.47d7wm3c3

Abstract

Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely distinguishable. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is based on the state-of-the-art U-Net, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error.

Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.

Methods

See manuscript for details of thorax EIT simulation data.

The experimental design of the FEM phantom comprises varying combinations of three spheres. The combinations represent the interactions between the contractions and expansions of the heart and lungs as shown in Fig 4. Fig 4(a) represents the simulation of the thorax with both of the lungs and heart, whereas Figs 4(b) and (c) represent the lungs and heart in the thorax, respectively. Fig 5 flowchart illustrates the experimental design used for generating FEM phantom and obtaining the reconstructed images for training data.

Funding

Ministry of Science and Technology, Taiwan, Award: 109-2221-E-006-073-MY2