# Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging

arXiv: https://arxiv.org/abs/2108.03233 · HTML: https://mlsanity.com/paper/2108.03233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Subspace Alignment For Domain Adaptation](https://mlsanity.com/paper/1409.5241/) (2014)
- [Time series classification for varying length series](https://mlsanity.com/paper/1910.04341/) (2019)

## Built on by (most cited first, 1 of 1)

- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2307.04771/) (2023)

## Abstract (arXiv)

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.