Abstract
Regional neuraxial analgesia is a safe method for pain relief during labor, but its effectiveness must be assessed carefully. As a non-invasive technique, thermal imaging is gaining increasing acclaim as an objective way to quantify blood flow redistribution-related warm modifications. Hence, thermal measurements are acquired under controlled conditions at different timestamps to determine the anesthesia depth by characterizing earlier thermal changes. However, the procedures during labor are limited by two main factors: a relatively small sample size is possible and thermal images cannot be acquired with both feet in the same position. This work implements an automatic semantic segmentation approach using five state-of-the-art deep learning architectures and an artifact removal algorithm based on morphological operators to deal with this problem. The obtained results are evaluated on two databases (acquired at the Universidad Nacional de Colombia sede Manizales and the SES Hospital Universitario de Caldas): controlled and uncontrolled environments for thermal data acquisition. Obtained results indicate that U-Mobilenetv2 approach outperforms the rest of the compared models.
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Acknowledgements
Under grants provided by the Minciencias’s project: “Herramienta de apoyo a la predicción de los efectos de anestéticos locales vía neuroaxial epidural a partir de termografía por infrarrojo" code 111984468021.
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Mejia-Zuluaga, R. et al. (2022). Deep Learning Semantic Segmentation of Feet Using Infrared Thermal Images. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_29
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