1 code implementation • 8 Nov 2023 • Alex Ling Yu Hung, Haoxin Zheng, Kai Zhao, Xiaoxi Du, Kaifeng Pang, Qi Miao, Steven S. Raman, Demetri Terzopoulos, Kyunghyun Sung
Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information.
no code implementations • 21 Jul 2023 • Kai Zhao, Alex Ling Yu Hung, Kaifeng Pang, Haoxin Zheng, Kyunghyun Sung
This observation inspired us to propose the Partial Diffusion Model (PartDiff), which diffuses the image to an intermediate latent state instead of pure random noise, where the intermediate latent state is approximated by the latent of diffusing the low-resolution image.
no code implementations • 21 Mar 2023 • Weinan Song, Haoxin Zheng, Dezhan Tu, Chengwen Liang, Lei He
Extensive experiments in simulated and real data show that our model significantly outperforms existing state-of-the-art models without learning from paired images or prior individual knowledge.
1 code implementation • 29 Mar 2022 • Alex Ling Yu Hung, Haoxin Zheng, Qi Miao, Steven S. Raman, Demetri Terzopoulos, Kyunghyun Sung
However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices.