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

Jules Scholler; Diana Mandache; Marie Christine Mathieu; Aïcha Ben Lakhdar; Marie Darche; Tual Monfort; Claude Boccara; Jean-Christophe Olivo-Marin; Kate Grieve; Vannary Meas-Yedid; Emilie Benoit a la Guillaume; Olivier Thouvenin

doi:10.1117/1.JMI.10.3.034504

1 June 2023 Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning

Jules Scholler, Diana Mandache, Marie Christine Mathieu, Aïcha Ben Lakhdar, Marie Darche, Tual Monfort, Claude Boccara, Jean-Christophe Olivo-Marin, Kate Grieve, Vannary Meas-Yedid, Emilie Benoit a la Guillaume, Olivier Thouvenin

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 3, 034504 (June 2023). https://doi.org/10.1117/1.JMI.10.3.034504

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 × 1.3 mm² 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 × 1.3 mm²) and above 96% at the specimen level (above cm²).

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Jules Scholler, Diana Mandache, Marie Christine Mathieu, Aïcha Ben Lakhdar, Marie Darche, Tual Monfort, Claude Boccara, Jean-Christophe Olivo-Marin, Kate Grieve, Vannary Meas-Yedid, Emilie Benoit a la Guillaume, and Olivier Thouvenin "Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning," Journal of Medical Imaging 10(3), 034504 (1 June 2023). https://doi.org/10.1117/1.JMI.10.3.034504

Received: 3 February 2023; Accepted: 9 May 2023; Published: 1 June 2023

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
23 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY