J
Jiang Liu
Researcher at Southern University of Science and Technology
Publications - 398
Citations - 11842
Jiang Liu is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Image segmentation & Optic cup (anatomical). The author has an hindex of 40, co-authored 367 publications receiving 7564 citations. Previous affiliations of Jiang Liu include Institute for Infocomm Research Singapore & National University of Singapore.
Papers
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Journal ArticleDOI
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Zaiwang Gu,Jun Cheng,Huazhu Fu,Kang Zhou,Huaying Hao,Yitian Zhao,Tianyang Zhang,Shenghua Gao,Jiang Liu +8 more
TL;DR: Comprehensive results show that the proposed CE-Net method outperforms the original U- net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation , cell contour segmentation and retinal optical coherence tomography layer segmentation.
Journal ArticleDOI
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Zaiwang Gu,Jun Cheng,Huazhu Fu,Kang Zhou,Huaying Hao,Yitian Zhao,Tianyang Zhang,Shenghua Gao,Jiang Liu +8 more
TL;DR: Li et al. as mentioned in this paper proposed a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation, which mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module.
Journal ArticleDOI
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
TL;DR: Zhang et al. as discussed by the authors proposed a multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function for OD and OC segmentation.
Journal ArticleDOI
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Jun Cheng,Jiang Liu,Yanwu Xu,Fengshou Yin,Damon Wing Kee Wong,Ngan Meng Tan,Dacheng Tao,Ching-Yu Cheng,Tin Aung,Tien Yin Wong +9 more
TL;DR: The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals and achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods.
Book ChapterDOI
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
TL;DR: This paper formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture based on a multi-scale and multi-level Convolutional Neural Network with a side-output layer to learn a rich hierarchical representation.