A color-based tumor segmentation method for clinical ex vivo breast
tissue assessment utilizing a multi-contrast brightfield imaging
strategy
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.