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Multispectral-Based Imaging and Machine Learning for Noninvasive Blood Loss Estimation

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13842))

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Abstract

Blood loss estimation during surgical operations is crucial in determining the appropriate transfusion decisions. More practical emerging solutions, e.g. the Triton System, use image processing and artificial intelligence (AI) in quantifying blood loss from images of blood-soaked sponges. Triton utilizes an infrared or depth camera that’s used to identify the region of color (RGB) image corresponding to a surgical textile. However, calculating depth is computationally expensive and can provide only the shape information. In this research, we propose a multispectral-based imaging and machine learning approach to directly quantify blood loss from images of surgical sponges. Near-infrared (NIR) and Visible (Vis) light sources in conjunction with an RGB imaging sensor without a NIR filter are used. With this, in addition to the improved focus and reduced background interference on the gauze image due to blood’s IR absorption capacities, the color as well as the shape information may be utilized. Results show that the multispectral-based imaging approach rendered a +28.30%, +48%, +27.97%, and 25.72% improvement on the MAE, MSE, RMSE, and MAPE, compared to using a single Vis wavelength or RGB image.

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Acknowledgement

DOST-ERDT Philippines is acknowledged for the funding and support.

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Authors and Affiliations

  1. University of the Philippines Visayas, Iloilo, Philippines

    Ara Abigail E. Ambita

  2. University of the Philippines Manila, Manila, Philippines

    Catherine S. Co

  3. University of the Philippines Diliman, Quezon City, Philippines

    Laura T. David, Charissa M. Ferrera & Prospero C. Naval Jr.

Authors
  1. Ara Abigail E. Ambita
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  2. Catherine S. Co
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  3. Laura T. David
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  4. Charissa M. Ferrera
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  5. Prospero C. Naval Jr.
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Corresponding author

Correspondence to Ara Abigail E. Ambita .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Ambita, A.A.E., Co, C.S., David, L.T., Ferrera, C.M., Naval, P.C. (2023). Multispectral-Based Imaging and Machine Learning for Noninvasive Blood Loss Estimation. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13842. Springer, Cham. https://doi.org/10.1007/978-3-031-26284-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-26284-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26283-8

  • Online ISBN: 978-3-031-26284-5

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