Vahideh Ghobadi

Papers

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About

Vahideh Ghobadi is a biomedical engineer whose research focuses on advancing computer-assisted surgery through deep learning and medical image analysis. Her most prominent work centers on real-time organ segmentation during laparoscopic procedures, particularly addressing the challenge of robust liver and gallbladder segmentation in cholecystectomy. In her highly cited 2024 study, she systematically analyzed how variations in camera models, imaging parameters, and institutional annotation methods affect the inference performance of convolutional neural networks, identifying optimal strategies to ensure consistent segmentation across diverse clinical datasets. This contribution is critical for making AI-assisted surgery reliable in real-world operating rooms. With over 3 citations on this recent work alone, Ghobadi’s research is gaining recognition for tackling a key bottleneck in translating deep learning models from controlled research settings to heterogeneous surgical environments. Her work not only improves intraoperative guidance for surgeons but also sets methodological standards for robust model deployment in medical imaging. Ghobadi’s efforts are paving the way for safer, more accurate laparoscopic interventions, marking her as an emerging leader in surgical AI.