Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Feet Segmentation for Regional Analgesia Monitoring using Convolutional RFF and Layer-Wise Weighted CAM Interpretability

Version 1 : Received: 9 May 2023 / Approved: 9 May 2023 / Online: 9 May 2023 (14:52:00 CEST)

A peer-reviewed article of this Preprint also exists.

Aguirre-Arango, J.C.; Álvarez-Meza, A.M.; Castellanos-Dominguez, G. Feet Segmentation for Regional Analgesia Monitoring Using Convolutional RFF and Layer-Wise Weighted CAM Interpretability. Computation 2023, 11, 113. Aguirre-Arango, J.C.; Álvarez-Meza, A.M.; Castellanos-Dominguez, G. Feet Segmentation for Regional Analgesia Monitoring Using Convolutional RFF and Layer-Wise Weighted CAM Interpretability. Computation 2023, 11, 113.

Abstract

The administration of regional neuraxial analgesia for pain relief during labor is widely recognized as a safe and effective method involving medication delivery into the epidural or subarachnoid space in the lower back. This study proposes an innovative semantic image segmentation methodology emphasizing enhanced interpretability using convolutional Random Fourier Features and layer-wise weighted class-activation maps tailored explicitly for foot segmentation in regional analgesia monitoring. Namely, our contribution is twofold: i) a novel Random Fourier Features layer is introduced to deal with image data to enhance three well-known architectures (FCN, UNet, and ResUNet); ii) three novel quantitive measures are presented to evaluate the interpretability of a given deep learning model devoted to segmentation tasks. Our approach is rigorously evaluated on a demanding dataset of foot thermal images from pregnant women who received epidural anesthesia. Its small size and considerable variability characterize the dataset. Our validation results demonstrate that the proposed methodology not only achieves competitive foot segmentation performance but also significantly enhances the explainability of the process, rendering it well-suited for applications such as epidural insertion during labor.

Keywords

Infrared Thermal Segmentation; Regional Neuraxial Analgesia; Deep Learning; Random Fourier Features; Class Activation Maps

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.