![]() ![]() These include, for example, respiratory, neurological, or cognitive conditions. If the infants survive, they are at increased risk of various physical and psychological illnesses, both short and long-term. Globally, premature birth is the single most common cause of death among children below the age of five and accounts for over one million deaths per year. Robust results were obtained by applying feature fusion, cross-modality transfer learning and classical augmentation strategies.Īs a preterm infant’s development inside the mother’s womb may not be fully completed, preterm birth can lead to serious health issues. The presented multi-modal neural networks represent a new approach to the problem of infant body segmentation with limited available data. The results of the individual classes showed that all body parts were well-segmented, only the accuracy on the torso is inferior since the models struggle when only small areas of the skin are visible. Only the thermography model achieved a lower accuracy (mIoU of 0.75). The fusion model achieved the best results during the final evaluation with a mean Intersection-over-Union (mIoU) of 0.85, closely followed by the RGB model. Individual optimization of the three deep learning models revealed that transfer learning and data augmentation improved segmentation regardless of the imaging modality. In addition, we used transfer learning on publicly available datasets of adults in combination with data augmentation to improve the segmentation results. For training and evaluation, a dataset containing 600 visible light and 600 thermography images from 20 recordings of infants was created and manually labeled. While the first two only used one imaging modality (visible light or thermography), the third applied a feature fusion of both. Based on a U-Net architecture, three neural networks were developed and compared. ![]() This work presents and evaluates algorithms for automatic segmentation of infant body parts using deep learning methods. For monitoring use in clinical practice, automatic segmentation of the different body regions is necessary due to the movement of the infant. Thermography may be a non-contact and wireless alternative to state-of-the-art, cable-based methods. Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis.
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