Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging

Schlereth M, Stromer D, Mantri Y, Tsujimoto J, Breininger K, Maier A, Anderson C, Garimella PS, Jokerst JV (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 261-266

Conference Proceedings Title: Informatik aktuell

Event location: Heidelberg, DEU

ISBN: 9783658369316

DOI: 10.1007/978-3-658-36932-3_56

Abstract

Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further.We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.

Authors with CRIS profile

Involved external institutions

University of California, San Diego United States (USA) (US)

How to cite

APA:

Schlereth, M., Stromer, D., Mantri, Y., Tsujimoto, J., Breininger, K., Maier, A.,... Jokerst, J.V. (2022). Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging. In Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 261-266). Heidelberg, DEU: Springer Science and Business Media Deutschland GmbH.

MLA:

Schlereth, Maja, et al. "Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging." Proceedings of the German Workshop on Medical Image Computing, 2022, Heidelberg, DEU Ed. Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2022. 261-266.

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