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Zailiang Chen

Bio: Zailiang Chen is an academic researcher from Central South University. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 12, co-authored 41 publications receiving 471 citations. Previous affiliations of Zailiang Chen include China Mobile & Chinese Ministry of Education.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: A supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel, which has potential applications for real-time computer-aided diagnosis and disease screening and on a new Retinal Images for Screening (RIS) database.

136 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed location-to-segmentation strategy achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly.

74 citations

Journal ArticleDOI
TL;DR: An efficient method based on generative adversarial network is proposed to reduce the speckle noise and preserve the texture details in OCT images and achieves a better denoising effectiveness.

70 citations

Journal ArticleDOI
TL;DR: A direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in whichCDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled, and which can achieve a lower average CDR error and higher correlation.
Abstract: Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image with a convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0.0563 and a higher correlation of around 0.726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0.905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

47 citations

Journal ArticleDOI
17 Jul 2019
TL;DR: Experimental results show that the proposed Weakly-Supervised Multi-Task Learning method effectively and simultaneously delivers evidence identification, optic disc segmentation, and accurate glaucoma diagnosis (92.6% TP Dice), which endows the WSMTL a great potential for the effective clinical assessment of glau coma.
Abstract: Evidence identification, optic disc segmentation and automated glaucoma diagnosis are the most clinically significant tasks for clinicians to assess fundus images. However, delivering the three tasks simultaneously is extremely challenging due to the high variability of fundus structure and lack of datasets with complete annotations. In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis. The WSMTL method only uses weak-label data with binary diagnostic labels (normal/glaucoma) for training, while obtains pixel-level segmentation mask and diagnosis for testing. The WSMTL is constituted by a skip and densely connected CNN to capture multi-scale discriminative representation of fundus structure; a well-designed pyramid integration structure to generate high-resolution evidence map for evidence identification, in which the pixels with higher value represent higher confidence to highlight the abnormalities; a constrained clustering branch for optic disc segmentation; and a fully-connected discriminator for automated glaucoma diagnosis. Experimental results show that our proposed WSMTL effectively and simultaneously delivers evidence identification, optic disc segmentation (89.6% TP Dice), and accurate glaucoma diagnosis (92.4% AUC). This endows our WSMTL a great potential for the effective clinical assessment of glaucoma.

40 citations


Cited by
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Journal ArticleDOI
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

425 citations

Journal ArticleDOI
Kai Hu1, Zhenzhen Zhang1, Xiaorui Niu1, Yuan Zhang1, Chunhong Cao1, Fen Xiao1, Xieping Gao1 
TL;DR: A novel retinal vessel segmentation method of the fundus images based on convolutional neural network (CNN) and fully connected conditional random fields (CRFs) which allows for detection of more tiny blood vessels and more precise locating of the edges.

233 citations

Journal ArticleDOI
TL;DR: This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress ofbig data analysis of medical images and the work of the team in theField of bigData analysis ofmedical imagec, especially the classification and segmentation ofmedical images.
Abstract: Big medical data mainly include electronic health record data, medical image data, gene information data, etc. Among them, medical image data account for the vast majority of medical data at this stage. How to apply big medical data to clinical practice? This is an issue of great concern to medical and computer researchers, and intelligent imaging and deep learning provide a good answer. This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress of big data analysis of medical images and the work of our team in the field of big data analysis of medical imagec, especially the classification and segmentation of medical images.

191 citations

Journal ArticleDOI
TL;DR: A convolutional neural network is developed and trained to automatically and simultaneously segment optic disc, fovea and blood vessels and can be used not just to segment blood vessels, but also optic disc and foveA with good accuracy.

189 citations