Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data. Academic Article uri icon

Overview

abstract

  • SIGNIFICANCE: The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently. AIM: This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages. APPROACH: 16 RESULTS: The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors. CONCLUSIONS: The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

publication date

  • February 6, 2023

Research

keywords

  • Breast Neoplasms

Identity

PubMed Central ID

  • PMC9900678

Scopus Document Identifier

  • 85147790250

Digital Object Identifier (DOI)

  • 10.1117/1.JBO.28.2.026001

PubMed ID

  • 36761256

Additional Document Info

volume

  • 28

issue

  • 2