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Quantitative characterization of human breast tissue based on deep learning segmentation of 3D optical coherence tomography images
Authors:Yuwei Liu  Roberto Adamson  Mark Galan  Basil Hubbi  Xuan Liu
Institution:1.Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, New Jersey 07105, USA;2.Rutgers University/New Jersey Medical School, Newark New Jersey 07103, USA;3.Overlook Medical Center, Summit, New Jersey 07901, USA
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.
Keywords:
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