ThermoLabAnimal – A high-throughput analysis software for non-invasive thermal assessment of laboratory mice

Abstract

Body temperature changes in laboratory mice are often assessed by invasive and stressful methods, which may confound the measurement. Infrared thermography is a possible non-invasive alternative, but the cost of standard thermal cameras, lack of dedicated software for biomedical purposes, and labour-intensiveness of thermal image analysis have limited their use. An additional limitation lies on the scarcity of research on the causing factors of differences between body surface and core body temperature. We propose a method for automatic assessment of mean body surface temperature in freely-moving mice, using dedicated software for thermal image analysis. While skin surface temperature may not necessarily be linearly correlated with core body temperature (in itself an imprecise concept), under standardized environmental conditions, such as those in which laboratory animals are kept, mean body surface temperature can provide useful information on their thermal status (i.e. deviations from normothermia, namely hypo-and hyperthermia). We developed a publicly available software that includes an imaging analysis workflow/algorithm for automatic segmentation of the pixels associated with the animal from the pixels associated with the background, removing the need for manually defining the area of analysis. A batch analysis mode is also available, for automatic and high-throughput analysis of all image files located in a folder. The software is compatible with the most widespread thermal camera manufacturer, 'FLIR Systems', as well as with the low-cost 'Thermal Expert TE-Q1' miniaturized high-resolution thermal camera used for this study. Furthermore, the software has been validated in a mouse model expressing nontransient hypothermia, where the thermal analysis results were compared with readings from implanted thermo-sensitive passive integrated transponders tags. Thermography allows for thermal assessment of laboratory animals without the effect of handling stress on their physiology or behaviour. Our automatic image analysis software also removes observer errors and bias, while speeding up the data processing. Version: Postprint (identical content as published paper) This is a self-archived document from i3S -Instituto de Investigação e Inovação em Saúde in the University of Porto Open Repository For Open Access to more of our publications, please visit http://repositorio-aberto.up.pt/ A01/00 Version: Postprint (identical content as published paper) This is a self-archived document from i3S -Instituto de Investigação e Inovação em Saúde in the University of Porto Open Repository For Open Access to more of our publications, please visit http://repositorio-aberto.up.pt/

Infrared thermal imaging associated with pain in laboratory animals

“…IRT converts this infrared radiation into digital images that can be interpreted as a function of color and a numerical scale [18]. This method is increasingly being used to detect and monitor peripheral vascular disorders in laboratory animals [22] and small animal species [20,27] that may well have important applications in biomedical research [28]. But, of course, analyzing and interpreting thermographic images requires understanding Animal welfare 5 the fundamental concepts upon which thermoregulation operates in homeotherm species.…”

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

“…Thermal cameras were excluded from the review because they provide detailed information on temperature and stress response but less about behaviour. Moreover, we found very few papers in which their use was automated (but see for example Franco et al, 2019 [16]).…”

Deep Instance Segmentation of Laboratory Animals in Thermal Images

“…The automation of this analysis has undoubtedly many advantages, among which objectivity and standardization are one of the most desirable. As far as the position and motion analysis are easy and commonly used parameters [2], the action or behavior recognition are quite difficult to automatize. Existing systems for laboratory animals' behavior analysis are restricted by, for example, the number or the color of the objects [2].…”

“…As far as the position and motion analysis are easy and commonly used parameters [2], the action or behavior recognition are quite difficult to automatize. Existing systems for laboratory animals' behavior analysis are restricted by, for example, the number or the color of the objects [2]. The spectrum of behavior analysis performed by the available systems is usually limited to exploration, rest or grooming.…”

“…A clear temperature difference in thermal imaging greatly simplifies foreground-background segmentation. Usually, the Otsu's thresholding supported by morphology is enough [2,14]. Figure 1b demonstrates the result of Otsu's thresholding on thermal image.…”