no code implementations • 9 Mar 2024 • Wentao Liu, Bowen Liang, Weijin Xu, Tong Tian, Qingsheng Lu, Xipeng Pan, Haoyuan Li, Siyu Tian, Huihua Yang, Ruisheng Su
In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning.
no code implementations • 11 Sep 2023 • Wentao Liu, Tong Tian, Weijin Xu, Lemeng Wang, Haoyuan Li, Huihua Yang
Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
1 code implementation • 21 Jun 2023 • Wentao Liu, Tong Tian, Lemeng Wang, Weijin Xu, Lei LI, Haoyuan Li, Wenyi Zhao, Siyu Tian, Xipeng Pan, Huihua Yang, Feng Gao, Yiming Deng, Ruisheng Su
In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
1 code implementation • JBHI 2022 • Wentao Liu,Huihua Yang, Tong Tian, Zhiwei Cao, Xipeng Pan, Weijin Xu, Yang Jin, Feng Gao
The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity.
Ranked #1 on Retinal Vessel Segmentation on DRIVE
2 code implementations • 9 Mar 2022 • Wentao Liu, Tong Tian, Weijin Xu, Huihua Yang, Xipeng Pan, Songlin Yan, Lemeng Wang
In this paper, we propose a novel hybrid architecture for medical image segmentation called PHTrans, which parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance.