Breast Cancer Imaging – Diagnostic Algorithms
With the increased volume of data that is collected by imaging entire margins of tumor specimens, the need for automated methods to analyze and classify potentially suspicious areas in real time is critical. By looking at the individual axial scans, new classification methods based on the frequency analysis can yield real time guidance to suspicious regions of interest. This same technology can also be used to guide surgical needle biopsy procedures by providing information about the tissue at the needle tip potentially improving the diagnostic sampling rates.
Axial OCT scan data and frequency analysis showing differences between breast tissue types: (a,d) adipose, (b,e) tumor, and (c,f) stroma.
Automated computer diagnostic algorithms are being developed to differentiate breast tissue types using OCT data. Classification is shown for (a) combined, (b) Fourier-domain, and (c) periodicity analysis techniques.
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