Radiometric Thermal Imaging Dataset of Laboratory Rats with Anatomical Segmentation Masks

Abstract

Infrared thermography provides a contact-free, restraint-free method to record surface temperatures. It serves as a valuable marker for thermoregulatory responses in laboratory animal stress and pharmacology research. However, the analysis of these images is currently bottlenecked by the manual delineation of anatomical regions. To date, no public dataset has provided paired radiometric thermal frames of rats with pixel-level body-part labels. We present a dataset of 1,655 quality-controlled radiometric thermal frames from 25 laboratory rats. Each frame is paired with a dense four-class anatomical segmentation mask (background, head, body, and tail) and the raw $480 \times 640$ temperature matrix (rows $\times$ columns) in degrees Celsius. This ensures every label is registered directly to the physical temperature it describes rather than a color-mapped rendering. The frames originate from two pharmacological cohorts where interventions alter thermoregulation in opposite directions: ethanol, which induces peripheral vasodilation, and ketamine, which affects central thermoregulation. This provides a wide and physiologically diverse range of surface temperature regimes. Aggregated across the dataset, the per-class temperatures follow a head~$>$~body~$>$~tail ordering in physical units. To demonstrate that the data support pixel-level segmentation directly from the radiometric channel, we present an exploratory U-Net segmentation pipeline that attains a subject-level cross-validated mean intersection-over-union of $0.895 \pm 0.006$. The dataset provides a reuse-ready benchmark for thermal semantic segmentation and for downstream physiological and stress-phenotyping analyses.