Ms. Sara Bouhali Profile

Sara Bouhali

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Machine learning , Computer vision , Image processing

Publications (2)

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Proceedings Article | 4 April 2024 Presentation + Paper

3D quantification of lymphatic (dys)function in head and neck cancer survivorship

John Rasmussen, Sara Bouhali, Fred Velasquez, Eva Sevick-Muraca, Carolina Gutierrez, Ron Karni, Fatima Merchant

Proceedings Volume 12831, 128310C (2024) https://doi.org/10.1117/12.3002948

KEYWORDS: Lymphatic system, Cancer, Head, Neck, Point clouds, RGB color model, Cameras, Biological imaging, Algorithm development, Lymph nodes, Fluorescence imaging, Imaging systems

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Functional lymphatics are essential for removal and transport of cellular waste and excess fluid from regional tissues and are dependent upon contractile lymphangion activity for proximal drainage into the venous blood stream. Lymphatic insufficiency in patients with cancer-acquired lymphedema is manifested by progressive dermal backflow, or retrograde flow of lymph into dermal lymphatic capillaries. Prior studies using near-infrared fluorescence (NIRF) lymphatic imaging with ICG as a contrast agent, found that dermal backflow provides early indication of lymphedema onset which, when untreated, persists over months and years, but, with two weeks of physiotherapy, dermal backflow could be resolved or reduced in early head and neck cancer survivorship. Thus, the extent or area of dermal backflow may provide an accurate, longitudinal measure of progressing/improving lymphatic dysfunction. In this work, we develop hardware and software solutions to automate determination of dermal backflow area on 3-dimensional tissue surface profiles for entry into the medical record. Specifically, we incorporate a stereo depth module into our custom NIRF lymphatic imaging system for simultaneous acquisition of depth, color, and NIRF images. Using camera calibration techniques, NIRF images are mapped onto point clouds derived from depth images. Algorithms for image segmentation of dermal backflow and stitching of multiple point clouds for more complete representation of dermal backflow across complex 3-dimensional tissue surfaces are described. Non-clinical testing demonstrates ±3% errors in dermal backflow area determination, with clinical testing on head and neck cancer survivors underway to assess efficacy of physiotherapies provided early after cancer treatment.

Proceedings Article | 2 April 2024 Presentation + Paper

3D rendering and analysis of dermal backflow as an early indicator of cancer-acquired lymphedema using RGB-D and near-infrared fluorescence lymphatic imaging

Sara Bouhali, Fatima Merchant, Ron Karni, Carolina Gutierrez, John Rasmussen

Proceedings Volume 12930, 1293008 (2024) https://doi.org/10.1117/12.3005842

KEYWORDS: Cameras, Lymphatic system, Point clouds, RGB color model, Image segmentation, 3D image processing, Calibration, Fluorescence

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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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