Quantitative characterization of human breast tissue based on deep learning segmentation of 3D optical coherence tomography images

Document Type

Article

Publication Date

5-1-2021

Abstract

In this study, we performed dual-modality optical coherence tomography (OCT) characterization (volumetric OCT imaging and quantitative optical coherence elastography) on human breast tissue specimens. We trained and validated a U-Net for automatic image segmentation. Our results demonstrated that U-Net segmentation can be used to assist clinical diagnosis for breast cancer, and is a powerful enabling tool to advance our understanding of the characteristics for breast tissue. Based on the results obtained from U-Net segmentation of 3D OCT images, we demonstrated significant morphological heterogeneity in small breast specimens acquired through diagnostic biopsy. We also found that breast specimens affected by different pathologies had different structural characteristics. By correlating U-Net analysis of structural OCT images with mechanical measurement provided by quantitative optical coherence elastography, we showed that the change of mechanical properties in breast tissue is not directly due to the change in the amount of dense or porous tissue. c 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Identifier

85105326393 (Scopus)

Publication Title

Biomedical Optics Express

External Full Text Location

https://doi.org/10.1364/BOE.423224

e-ISSN

21567085

First Page

2647

Last Page

2660

Issue

5

Volume

12

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