Rafeef Abugharbieh
Papers
9
Total Citations
184
H-Index
8
About
Rafeef Abugharbieh is a leading researcher in medical image analysis and computer-assisted surgery, with a focus on enhancing robot-assisted minimally invasive procedures. Her work primarily addresses the challenges of real-time surgical scene understanding, particularly in robot-assisted partial nephrectomy (RAPN) for kidney cancer treatment. Abugharbieh’s major contributions include developing algorithms for simultaneous multi-structure segmentation and 3D nonrigid pose estimation in endoscopic video, enabling surgeons to perceive critical anatomical context during image-guided robotic surgery. She pioneered automatic segmentation of occluded vasculature using pulsatile motion analysis, a technique that helps surgeons identify hidden blood vessels in real time. Her research on robust dense endoscopic stereo reconstruction has advanced 3D surface modeling of organs, while her uncertainty-encoded augmented reality system provides surgeons with enhanced visual guidance during tumor resection. Abugharbieh’s work on biomechanical kidney models predicts tumor displacement under surgical pressure, improving resection accuracy. With over 180 citations across her most-cited papers, including influential studies on multi-modal image-guided tumor identification and efficient multi-organ segmentation, her contributions have significantly advanced the integration of pre- and intra-operative imaging for safer, more precise minimally invasive surgeries.
Research Focus
Key Achievements
Top Papers
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- 3Robust Dense Endoscopic Stereo Reconstruction for Minimally Invasive Surgery26 citations · 2013
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