11 citations · 12 across the 3 of their papers we have counts for
4 papers
RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation
Seunghwan Lee, Sangjoon Park, Jongmin Lee +4
Tumor delineation is required for radiotherapy planning, yet automatic segmentation is limited by errors, predominantly false positives, that distort dose and increase editing time…
A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation
Yoon Jo Kim, Wonyoung Cho, Jongmin Lee +7
Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate thi…
InFusionSurf: Refining Neural RGB-D Surface Reconstruction Using Per-Frame Intrinsic Refinement and TSDF Fusion Prior Learning
Seunghwan Lee, Gwanmo Park, Hyewon Son +2
We introduce InFusionSurf, an innovative enhancement for neural radiance field (NeRF) frameworks in 3D surface reconstruction using RGB-D video frames. Building upon previous metho…
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
Han Joo Chae, Seunghwan Lee, Hyewon Son +2
We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers duri…