Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network

PLoS One. 2022 Jul 21;17(7):e0271377. doi: 10.1371/journal.pone.0271377. eCollection 2022.

Abstract

MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Breast / diagnostic imaging
  • Breast / pathology
  • Breast Neoplasms* / diagnostic imaging
  • Breast Neoplasms* / pathology
  • Diagnostic Imaging
  • Female
  • Humans
  • Microwave Imaging*
  • Microwaves
  • Neural Networks, Computer

Grants and funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the SME instrument phase 2 grant agreement No 830265. This project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 793449. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 872752. Lorenzo Sani, Alessandro Vispa and Giovanni Raspa are employed by UBT - Umbria Bioengineering Technologies. Such commercial funder provided support in the form of salaries for such authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.