RGB-D scene analysis in the NICU.

Continuity of care is achieved in the neonatal intensive care unit (NICU) through careful documentation of all events of clinical significance, including clinical interventions and routine care events (e.g., feeding, diaper change, weighing, etc.). As a step towards automating this documentation process, we propose a scene recognition algorithm that can automatically identify key features in a single image of the patient environment, paired with a rule-based sentence generator to caption the scene. Color and depth video were obtained from 29 newborn patients from the Children's Hospital of Eastern Ontario (CHEO) using an Intel RealSense SR300 RGB-D camera and manual bedside event annotation. Image processing techniques are implemented to classify two lighting conditions: brightness level and phototherapy. A deep neural network is developed for three image classification tasks: on-going intervention, bed occupancy, and patient coverage. Transfer learning is leveraged in the feature extraction layers, such that weights learned from a generic data-rich task are applied to the clinical domain where data collection is complex and costly. Different depth fusion techniques are implemented and compared among classification tasks, where the depth and color data are fused as an RGB-D image (image fusion) or separately at various layers in the network (network fusion). Promising results were obtained with >84% sensitivity and >73% F1 measure across all context variables despite the large class imbalance. RGBD-based models are shown to outperform RGB models on most tasks. In general, a 4-channel image fusion and network fusion at the 11th layer of the VGG-16 architecture were preferred. Ultimately, achieving complete scene understanding through multimodal computer vision could form the basis for a semi-automated charting system to assist clinical staff.

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