Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks

Sanne E. Okel; Fons van der Sommen; Endi Selmanaj; Joost van der Putten; Maarten R. Struyvenberg; Jacques J.G.H.M. Bergman; Peter H. N. de With

doi:10.1117/12.2579751

15 February 2021 Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks

Sanne E. Okel, Fons van der Sommen, Endi Selmanaj, Joost van der Putten, Maarten R. Struyvenberg, Jacques J.G.H.M. Bergman, Peter H. N. de With

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972H (2021) https://doi.org/10.1117/12.2579751
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Computer-aided detection (CAD) approaches have shown promising results for early esophageal cancer detection using Volumetric Laser Endoscopy (VLE) imagery. However, the relatively slow and computationally costly tissue segmentation employed in these approaches hamper their clinical applicability. In this paper, we propose to reframe the 2D tissue segmentation problem into a 1D tissue boundary detection problem. Instead of using an encoder-decoder architecture, we propose to follow the tissue boundary using a Recurrent Neural Network (RNN), exploiting the spatio-temporal relations within VLE frames. We demonstrate a near state-of-the-art performance using 18 times less floating point operations, enabling real-time execution in clinical practice.

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Sanne E. Okel, Fons van der Sommen, Endi Selmanaj, Joost van der Putten, Maarten R. Struyvenberg, Jacques J.G.H.M. Bergman, and Peter H. N. de With "Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972H (15 February 2021); https://doi.org/10.1117/12.2579751

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