PurposeThe 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.ApproachWe 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 × 1.3 mm2 images and compared with standard H&E histology diagnosis.ResultsUsing our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3 × 1.3 mm2) and above 96% at the specimen level (above cm2).ConclusionsAltogether, 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.
Dynamic FFOCT allows us to record the intrinsic motion of biological samples in 3D, over hours. We performed scratch assays on primary porcine RPE and human induced pluripotent stem cells derived RPE cell cultures. We plotted motion maps from the optical flow. For wounds <40µm, the cell layer close the wound at different speeds depending on the type of RPE cells. For bigger wounds, the cell layer retract, mimicking degenerative diseases. Comparison between Dynamic FFOCT images and Immuno-chemistry images showed that mitochondria may contribute to the dynamic profile of cells. Dynamic FFOCT can be useful for the study of regenerative medicine.
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