Abstract
Infrared thermography (IRT) is widely used to assess skin temperature in response to physiological changes. Yet, it remains challenging to standardize skin temperature measurements over repeated datasets. We developed an open-access semi-automated segmentation tool (the IRT-toolbox) for measuring skin temperatures in the thoracic area to estimate supraclavicular brown adipose tissue (scBAT) activity, and compared it to manual segmentations. The IRT-toolbox, designed in Python, consisted of image pre-alignment and non-rigid image registration. The toolbox was tested using datasets of 10 individuals (BMI = 22.1 ± 2.1 kg/m(2), age = 22.0 ± 3.7 years) who underwent two cooling procedures, yielding four images per individual. Regions of interest (ROIs) were delineated by two raters in the scBAT and deltoid areas on baseline images. The toolbox enabled direct transfer of baseline ROIs to the registered follow-up images. For comparison, both raters also manually drew ROIs in all follow-up images. Spatial ROI overlap between methods and raters was determined using the Dice coefficient. Mean bias and 95% limits of agreement in mean skin temperature between methods and raters were assessed using Bland-Altman analyses. ROI delineation time was four times faster with the IRT-toolbox (01:04 min) than with manual delineations (04:12 min). In both anatomical areas, there was a large variability in ROI placement between methods. Yet, relatively small skin temperature differences were found between methods (scBAT: 0.10 °C, 95%LoA[-0.13 to 0.33 °C] and deltoid: 0.05 °C, 95%LoA[-0.46 to 0.55 °C]). The variability in skin temperature between raters was comparable between methods. The IRT-toolbox enables faster ROI delineations, while maintaining inter-user reliability compared to manual delineations. (Trial registration number (ClinicalTrials.gov): NCT04406922, [May 29, 2020]).