Hong Rock Choi

Starlab (United States)

Papers

2

Total Citations

36

H-Index

2

About

Hong Rock Choi is at the forefront of applying deep learning to robotic surgery, with a focused expertise in semantic segmentation for urological procedures. His pioneering work centers on developing real-time, intraoperative AI systems that can automatically identify and delineate anatomical structures during robotic prostatectomy. Choi’s major contributions include the creation and validation of deep learning models that achieve high-accuracy semantic segmentation in live surgical video, a critical step toward enhancing surgical precision and reducing complications. His most cited 2024 paper, which has already garnered 27 citations, demonstrates the feasibility of real-time segmentation during surgery, while a companion study (9 citations) provides a rigorous comparison of convolutional neural networks versus visual transformers for this task. This comparative work is notable for guiding the field toward optimal architectures for surgical vision. By bridging computer vision and robotic surgery, Choi’s research is laying the groundwork for smarter, more autonomous surgical assistance systems. His work is essential reading for anyone interested in the intersection of deep learning, real-time video analysis, and minimally invasive surgery.

Research Focus

Key Achievements

2
H-Index
2
Papers
36
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
Deep Learning Model for Real‑time Semantic Segmentation During Intraoperative Robotic Prostatectomy
27 citations · 2024
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: Starlab (United States)

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago