activity
20182021
most citedMulti-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

3 citations · 3 across the 3 of their papers we have counts for

collaborators

6 papers

cs.NE2021

Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization

Pinyuan Zhong, Yue Zhang, Xiaoying Tang

In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the propo…

eess.IV20213 cited

Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

Yue Zhang, Chengtao Peng, Liying Peng +8

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usuall…

eess.IV2021

PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images

Yingying Xu, Ming Cai, Lanfen Lin +10

In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is new…

eess.IV2019

Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction

Yue Zhang, Jiong Wu, Yu Zhou +2

In this paper, we formulated the kidney segmentation task in a coarse-to-fine fashion, predicting a coarse label based on the entire CT image and a fine label based on the coarse s…

eess.IV2019

Prostate segmentation using Z-net

Yue Zhang, Jiong Wu, Wanli Chen +2

In this paper, we proposed a novel architecture of convolutional neural network (CNN), namely Z-net, for segmenting prostate from magnetic resonance images (MRIs). In the proposed…

cs.CV2018

Prostate Segmentation using 2D Bridged U-net

Wanli Chen, Yue Zhang, Junjun He +4

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to trai…