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

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.