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

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.