Soo In Lee
Papers
1
Total Citations
2
H-Index
1
About
Soo In Lee is a researcher whose work sits at the intersection of biomedical engineering and computer vision, with a particular focus on advancing automated systems for hair restoration surgery. Her key research area involves developing intelligent algorithms for the robotic classification and harvesting of follicular units (FUs)—the natural groupings of hair used in transplants. Her most notable contribution, the 2016 paper "Follicular Unit Classification Method Using Angle Variation of Boundary Vector for Automatic Hair Implant System," introduces a novel computational technique that analyzes the angle variation of boundary vectors in FU images to accurately classify units by the number of hairs they contain. This method directly enhances the precision of systems like the ARTAS robotic harvest platform, which relies on digital imaging to distinguish between single, double, and triple hair FUs. While her citation count of 2 reflects a specialized niche, the work represents a meaningful step toward automating a delicate surgical process, reducing human error, and improving graft survival rates. Lee’s research bridges the gap between image processing and clinical dermatology, offering a practical solution for more consistent, data-driven hair transplant outcomes.
Research Focus
Key Achievements
Top Papers
- 1