One hundred percent successful automatic breast cancer diagnosis using static and dynamic FFOCT images (Conference Presentation)

Jules Scholler; Olivier Thouvenin; Emilie Benoit a la Guillaume; Claude Boccara

doi:10.1117/12.2544301

10 March 2020 One hundred percent successful automatic breast cancer diagnosis using static and dynamic FFOCT images (Conference Presentation)

Jules Scholler, Olivier Thouvenin, Emilie Benoit a la Guillaume, Claude Boccara

Author Affiliations +

Proceedings Volume 11222, Molecular-Guided Surgery: Molecules, Devices, and Applications VI; 1122206 (2020) https://doi.org/10.1117/12.2544301
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

In this project, we analyzed 30 healthy and tumorous breast samples using static and dynamic full field optical coherence tomography (FF-OCT). We developed an automatic analysis workflow to classify each sample and compared it to an independent standard histological diagnosis. We used a first machine-learning algorithm to obtain cell and fiber segmentation of FF-OCT images and applied a linear support vector machine (SVM) analysis to classify each sample. We could obtain 100% specificity and sensitivity compared to histology. The label-free and non-invasive combination of static and dynamic FF-OCT thus appears very promising to obtain an efficient diagnosis of tumoral samples.

Conference Presentation

Citation Download Citation

Jules Scholler, Olivier Thouvenin, Emilie Benoit a la Guillaume, and Claude Boccara "One hundred percent successful automatic breast cancer diagnosis using static and dynamic FFOCT images (Conference Presentation)", Proc. SPIE 11222, Molecular-Guided Surgery: Molecules, Devices, and Applications VI, 1122206 (10 March 2020); https://doi.org/10.1117/12.2544301

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