Thermography as an indicator of goat welfare in an intensive production system

This study evaluated the welfare of Saanen, Moxoto, and Anglo-Nubian goats kept in collective or individual pens for a feedlot system, evaluated with infrared thermography. A total of twenty-four goats were used, eight for each breed. Animals were distributed in a completely randomized design, with a 2 × 3 factorial with two fixed effects: housing type (collective or individual pens) and breed (Moxoto, Saanen, and Anglo-Nubian). The surface temperature was evaluated using an infrared thermographic camera, and behavioral analysis was based on the qualitative behavior assessment using a fixed list of descriptors. The breed was not different for all behavior evaluations and surface temperature (p>0.05). There was a difference between the housing types, where the collective pens showed goats more agitated, frustrated, and sociable (p<0.05). There was an influence of agitated, apathetic, frustrated, attentive, and curious behaviors on surface temperatures, in which feet and body temperatures decreased in these goats. (p<0.05). Moxoto, Anglo-Nubian, and Saanen goats showed similar behavior even when kept in collective or individual pens. Individual pens can restrict the goats' social relationships but reduce negative behaviors such as irritation and frustration. The lower foot temperatures of feedlot goats are related to the attention behavior in 86.75% of the observations.

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images

BACKGROUND

Two types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected.

RESULTS

Given the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method.

CONCLUSIONS

The proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH .

A hand motion capture method based on infrared thermography for measuring fine motor skills in biomedicine

Many biomedical applications require fine motor skill assessments; however, real-time and contactless fine motor skill assessments are not typically implemented. In this study, we followed the 2D-to-3D pipeline principle and proposed a transformer-based spatial-temporal network to accurately regress 3D hand joint locations by inputting infrared thermal video for eliminating need of multiple cameras or RGB-D devices. We also developed a dataset composed of infrared thermal videos and ground truth annotations for training. The label represents a set of 3D joint locations from infrared optical trackers, which is considered the gold standard for clinical applications. To demonstrate their potential, the proposed method was used to measure the finger motion angle, and we investigated its accuracy by comparing the proposal with the Azure Kinect system and Leap Motion system. On the proposed dataset, the proposed method achieved a 3D hand pose mean error of less than 14 mm and outperforms the other deep learning methods. When the error thresholds were larger than approximately 35 mm, our method first to achieved excellent performance (>80%) in terms of the fraction of good frames. For the finger motion angle calculation task, the proposed and commercial systems had comparable inter-system reliability (ICC_2,1 ranging from 0.81 to 0.83) and excellent validity (Pearson's r-values ranging from 0.82 to 0.86). We believe that the proposed approaches can capture hand motion and measure finger motion angles and can be used in different biomedicine scenarios as an effective evaluation tool for fine motor skills.

Compensation method for the influence of angle of view on animal temperature measurement using thermal imaging camera combined with depth image

In the study, we proposed an animal surface temperature measurement method based on Kinect sensor and infrared thermal imager to facilitate the screening of animals with febrile diseases. Due to random motion and small surface temperature variation of animals, the influence of the angle of view on temperature measurement is significant. The method proposed in the present study could compensate the temperature measurement error caused by the angle of view. Firstly, we analyzed the relationship between measured temperature and angle of view and established the mathematical model for compensating the influence of the angle of view with the correlation coefficient above 0.99. Secondly, the fusion method of depth and infrared thermal images was established for synchronous image capture with Kinect sensor and infrared thermal imager and the angle of view of each pixel was calculated. According to experimental results, without compensation treatment, the temperature image measured in the angle of view of 74° to 76° showed the difference of more than 2°C compared with that measured in the angle of view of 0°. However, after compensation treatment, the temperature difference range was only 0.03-1.2°C. This method is applicable for real-time compensation of errors caused by the angle of view during the temperature measurement process with the infrared thermal imager.