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

Yuwei Liu, Roberto Adamson, Mark Galan, Basil Hubbi, Xuan Liu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)2647-2660
Number of pages14
JournalBiomedical Optics Express
Volume12
Issue number5
DOIs
StatePublished - May 2021

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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