Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning

Abstract

Purpose: The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration.

Approach: We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual $1.3\times 1.3{\mathrm{mm}}^2$ images and compared with standard H&E histology diagnosis.

Results: Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level ($1.3\times 1.3{\mathrm{mm}}^2$) and above 96% at the specimen level (above ${\mathrm{cm}}^2$).

Conclusions: Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.

Keywords: automated diagnosis; dynamic optical coherence tomography; label-free histopathology; machine learning; metabolic imaging.