Eric Feliberti
Papers
3
Total Citations
18
H-Index
2
About
Eric Feliberti is a biomedical engineer and researcher specializing in medical robotics, surgical planning, and deep learning-based medical image segmentation. His work sits at the intersection of artificial intelligence and minimally invasive surgery, with a focus on developing practical tools that enhance surgical precision and patient outcomes. Feliberti's most significant contributions center on applying advanced neural network architectures to breast MRI analysis. His 2022 work introducing an nnU-Net-based multi-modality segmentation framework for breast tissue delineation — garnering 14 citations — represents a meaningful step forward in preoperative planning for robotic tumor surgery, enabling more accurate identification of breast masses through automated image segmentation. A closely related study further extends this foundation by proposing cascaded deep neural network architectures tailored for surgical navigation. Beyond imaging, Feliberti has demonstrated a hands-on engineering approach through his design of RoboCatch, a novel hand-held robotic instrument engineered to enable spillage-free specimen retrieval during laparoscopic procedures — addressing a critical clinical challenge with an elegant mechanical solution. Across his portfolio, Feliberti consistently bridges computational innovation with real-world surgical application, making his research particularly valuable to engineers, clinicians, and students working at the frontier of intelligent surgical systems.
Research Focus
Key Achievements
Top Papers
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