15 citations · 34 across the 6 of their papers we have counts for
7 papers
MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting
Chengkai Wang, Luoyu Hong, Yiting Zhao +7
High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical…
IE-SRGS: An Internal-External Knowledge Fusion Framework for High-Fidelity 3D Gaussian Splatting Super-Resolution
Xiang Feng, Tieshi Zhong, Shuo Chang +7
Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. E…
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
Yaqi Wang, Yifan Zhang, Xiaodiao Chen +24
Computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the…
SRGS: Super-Resolution 3D Gaussian Splatting
Xiang Feng, Yongbo He, Linxi Chen +8
Low-resolution (LR) multi-view capture limits the fidelity of 3D Gaussian Splatting (3DGS). 3DGS super-resolution (SR) is therefore important, yet challenging because it must recov…
ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields
Xiang Feng, Yongbo He, Yubo Wang +6
Neural Radiance Fields (NeRF) have achieved great success in the task of synthesizing novel views that preserve the same resolution as the training views. However, it is challengin…
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Xiang Feng, Chengkai Wang, Chengyu Wu +4
Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentati…
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Yunxiang Li, Bowen Jing, Zihan Li +2
Automatic segmentation of medical images is crucial in modern clinical workflows. The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without sp…