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Lymphatics are crucial in maintaining cardiovascular health and facilitating immune surveillance, yet their significance is often overlooked in medical practice. One notable consequence of cancer treatment, affecting a growing population of survivors, is cancer-acquired lymphedema (LE), a prevalent and incurable condition diagnosed by increased tissue volumes. A recent diagnostic finding, using Near-Infrared Fluorescence (NIRF) lymphatic imaging, indicates that dermal backflow, the retrograde flow of lymphatic fluid from collecting lymphatic vessels into the lymphatic capillaries, is predictive of LE. Dermal backflow contributes to the development of irreversible tissue changes associated with LE, including tissue swelling, accumulation of subcutaneous adipose tissue, and ultimately fibrosis. Evidence suggests that early intervention, prior to tissue swelling, may ameliorate LE, unfortunately, diagnostic methods for detecting early lymphatic dysfunction and monitoring the effectiveness of early interventions on the onset of LE are limited. In this work, we build a dedicated, quantitative NIRF lymphatic imaging system to assess dermal backflow and the impact of early physiotherapy on the progression of LE in head and neck cancer survivors. This system integrates NIRF and RGB-D stereo camera hardware and software for image acquisition, 3D rendering, stereo calibration, registration, and visualization of dermal backflow. Additionally, we develop software solutions to automate the segmentation and quantification of lymphatic dysfunction over complex 3D surface profiles. Our preliminary results demonstrate the accurate reconstruction of 3D models with a NIRF texture overlay using data from our NIRF and RGB-D stereo camera device. Furthermore, dermal backflow segmentation automation was accomplished in 2D NIRF images and 3D reconstructions of clinically relevant surfaces and then incorporated into the process of dermal backflow quantification.
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