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Proceedings Paper

One hundred percent successful automatic breast cancer diagnosis using static and dynamic FFOCT images (Conference Presentation)
Author(s): Jules Scholler; Olivier Thouvenin; Emilie Benoit a la Guillaume; Claude Boccara

Paper 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.

Paper Details

Date Published: 10 March 2020
Proc. SPIE 11222, Molecular-Guided Surgery: Molecules, Devices, and Applications VI, 1122206 (10 March 2020); doi: 10.1117/12.2544301
Show Author Affiliations
Jules Scholler, Institut Langevin Ondes et Images (France)
Olivier Thouvenin, Institut Langevin Ondes et Images (France)
Emilie Benoit a la Guillaume, LLTech SAS (France)
Claude Boccara, Institut Langevin Ondes et Images (France)

Published in SPIE Proceedings Vol. 11222:
Molecular-Guided Surgery: Molecules, Devices, and Applications VI
Sylvain Gioux; Summer L. Gibbs; Brian W. Pogue, Editor(s)

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