  Remote patient monitoring has emerged as a prominent non-invasive method,
using digital technologies and computer vision (CV) to replace traditional
invasive monitoring. While neonatal and pediatric departments embrace this
approach, Pediatric Intensive Care Units (PICUs) face the challenge of
occlusions hindering accurate image analysis and interpretation.
\textit{Objective}: In this study, we propose a hybrid approach to effectively
segment common occlusions encountered in remote monitoring applications within
PICUs. Our approach centers on creating a deep-learning pipeline for limited
training data scenarios. \textit{Methods}: First, a combination of the
well-established Google DeepLabV3+ segmentation model with the
transformer-based Segment Anything Model (SAM) is devised for occlusion
segmentation mask proposal and refinement. We then train and validate this
pipeline using a small dataset acquired from real-world PICU settings with a
Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of
85\%. \textit{Results}: Both quantitative and qualitative analyses underscore
the effectiveness of our proposed method. The proposed framework yields an
overall classification performance with 92.5\% accuracy, 93.8\% recall, 90.3\%
precision, and 92.0\% F1-score. Consequently, the proposed method consistently
improves the predictions across all metrics, with an average of 2.75\% gain in
performance compared to the baseline CNN-based framework. \textit{Conclusions}:
Our proposed hybrid approach significantly enhances the segmentation of
occlusions in remote patient monitoring within PICU settings. This advancement
contributes to improving the quality of care for pediatric patients, addressing
a critical need in clinical practice by ensuring more accurate and reliable
remote monitoring.
