Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging. (arXiv:2108.03233v1 [cs.CV])

Incorporating boundaries of the imaging object as a priori information to
imaging algorithms can significantly improve the performance of electromagnetic
medical imaging systems. To avoid overly complicating the system by using
different sensors and the adverse effect of the subject’s movement, a
learning-based method is proposed to estimate the boundary (external contour)
of the imaged object using the same electromagnetic imaging data. While imaging
techniques may discard the reflection coefficients for being dominant and
uninformative for imaging, these parameters are made use of for boundary
detection. The learned model is verified through independent clinical human
trials by using a head imaging system with a 16-element antenna array that
works across the band 0.7-1.6 GHz. The evaluation demonstrated that the model
achieves average dissimilarity of 0.012 in Hu-moment while detecting head
boundary. The model enables fast scan and image creation while eliminating the
need for additional devices for accurate boundary estimation.