Lucas Santana Lellis

Papers

Total Citations

H-Index

About

Lucas Santana Lellis is a researcher focused on advancing medical image analysis through interactive segmentation and contour tracking. His work addresses the critical challenge of efficiently and accurately delineating anatomical structures in medical images, a task essential for diagnosis and treatment planning. In his most cited paper, "Interactive Border Contour with Automatic Tracking Algorithm Selection for Medical Images" (2019), Lellis introduced a novel approach that dynamically selects the optimal tracking algorithm based on image features, enhancing the precision and adaptability of border detection. This contribution, while accumulating 3 citations, demonstrates his commitment to developing user-guided tools that balance automation with clinician oversight. Lellis’s research lies at the intersection of computer vision, machine learning, and biomedical engineering, aiming to reduce manual annotation burdens while improving diagnostic reliability. His work is particularly relevant for applications in radiology and pathology, where accurate contouring is vital. By focusing on interactive systems that empower medical professionals, Lellis contributes to the broader goal of integrating intelligent algorithms into clinical workflows, making medical image analysis more accessible and effective.