Fast body part segmentation and tracking of neonatal video data using deep learning

• Published in: Medical & biological engineering & computing Vol. 58; no. 12; pp. 3049 - 3061
• Main Authors: Antink, Christoph Hoog, Ferreira, Joana Carlos Mesquita, Paul, Michael, Lyra, Simon, Heimann, Konrad, Karthik, Srinivasa, Joseph, Jayaraj, Jayaraman, Kumutha, Orlikowsky, Thorsten, Sivaprakasam, Mohanasankar, Leonhardt, Steffen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Accuracy; Adults; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Body parts; Coders; Computer Applications; Computing time; Datasets; Deep Learning; Encoders-Decoders; Frames (data processing); Frames per second; Human Body; Human Physiology; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Imaging; Infant; Infant, Newborn; Infant, Premature; Infants; Injury prevention; Near infrared radiation; Neonates; Newborn babies; Original; Original Article; Photoplethysmography; Radiology; Real time; Skin; Skin injuries; Training; Video data; Deep learning; Nicu; Image processing; Semantic segmentation; Camera-based monitoring
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-020-02251-4

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates’ body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications. Graphical Abstract This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.