Medical thermography provides an overview of the human body with two-dimensional (2D) information that assists the identification of temperature changes, based on the analysis of surface distribution. However, this approach lacks spatial depth information, which can be enhanced by adding multiple images or three-dimensional (3D) systems. Therefore, the methodology applied for this paper generates a 3D point cloud (from thermal infrared images), a 3D geometry model (from CT images), and the segmented inner anatomical structures. Thus, the following computational processing was employed: Structure from Motion (SfM), image registration, and alignment (affine transformation) between the 3D models obtained to combine and unify them. This paper presents the 3D reconstruction and visualization of the respective geometry of the neck/bust and inner anatomical structures (thyroid, trachea, veins, and arteries). Additionally, it shows the whole 3D thermal geometry in different anatomical sections (i.e., coronal, sagittal, and axial), allowing it to be further examined by a medical team, improving pathological assessments. The generation of 3D thermal anatomy models allows for a combined visualization, i.e., functional and anatomical images of the neck region, achieving encouraging results. These 3D models bring correlation of the inner and outer regions, which could improve biomedical applications and future diagnosis with such a methodology.

The validation of methods for understanding the effects of many diseases and treatments requires the use of animal models in translational research. In this context, sheep have been employed extensively in scientific studies. However, the imposition of experimental conditions upon these animals may result in the experience of discomfort, pain, and stress. The ethical debates surrounding the use of animals in research have resulted in the adoption of Directive 2010/63/EU. The present study proposes a non-contact method for monitoring the respiration rate of sheep based on video processing. The Detecron2 model was trained to segment the sheep’s body, abdominal, and facial regions in the video frames. A motion-tracking algorithm was developed to assess abdominal movement associated with the sheep’s respiratory cycle. The method was applied to videos of Rhön sheep under experimental and housing conditions, utilising two types of cameras to assess the effectiveness of the proposed approach. The mean average error (MAE) obtained was 0.79 breaths/minute for the visible and 1.83 breaths/minute for the near-infrared (NIR) method. This study demonstrates the feasibility of video technology for simultaneous and non-invasive respiration monitoring, being a crucial parameter for assessing the health deterioration of multiple laboratory animals.