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A color-based tumor segmentation method for clinical ex vivo breast tissue assessment utilizing a multi-contrast brightfield imaging strategy
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  • Roujia Wang,
  • Lillian Ekem,
  • Jennifer Gallagher,
  • Rachel E. Factor,
  • Allison Hall,
  • Nirmala Ramanujam
Roujia Wang
Duke University Department of Biomedical Engineering

Corresponding Author:[email protected]

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Lillian Ekem
Duke University Department of Biomedical Engineering
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Jennifer Gallagher
Duke University Department of Surgery
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Rachel E. Factor
Duke University Department of Pathology
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Allison Hall
Duke University Department of Pathology
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Nirmala Ramanujam
Duke University Department of Biomedical Engineering
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Abstract

We demonstrate an automated two-step tumor segmentation method leveraging color information from brightfield images of fresh core needle biopsies of breast tissue. Three different color spaces (HSV, CIELAB, YCbCr) were explored for the segmentation task. By leveraging white-light and green-light images, we identified two different types of color transformations that could separate adipose from benign and tumor or cancerous tissue. We leveraged these two distinct color transformation methods in a two-step process where adipose tissue segmentation was followed by benign tissue segmentation thereby isolating the malignant region of the biopsy. Our tumor segmentation algorithm and imaging probe could highlight suspicious regions on unprocessed biopsy tissue to guide selection of areas most similar to malignant tissues for tissue pathology whether it be formalin fixed or frozen sections, expedite tissue selection for molecular testing, detect positive tumor margins, or serve an alternative to tissue pathology, in countries where these services are lacking.
23 Jun 2023Submitted to Journal of Biophotonics
23 Jun 2023Submission Checks Completed
23 Jun 2023Assigned to Editor
23 Jun 2023Review(s) Completed, Editorial Evaluation Pending
23 Jun 2023Reviewer(s) Assigned
19 Oct 2023Editorial Decision: Revise Major