NDLI: Weakly supervised retinal vessel segmentation algorithm without groundtruth

Content Provider	IET Digital Library
Author	Lu, Zheng Chen, Dali Xue, Dingyu
Abstract	In the current image processing field, medical image segmentation needs a lot of groundtruths, and the process of making these groundtruths is time-consuming and laborious. Thus, a novel retinal vessel segmentation algorithm without groundtruth is proposed in this Letter. The hierarchical clustering algorithm is first used to binary classify vessel and non-vessel pixels. Then classification results based on DRIVE databases are used as pseudo groundtruths to train the neural networks and transfer learning is considered for subsequent processing. Next the trained network is used as the feature extraction tool, by calculating and comparing the difference of image features between the target domain data (DRIVE database) and the source domain data (STARE, CHASE DB1, and HRF databases) extracted from the network. The data required for training is expanded based on semi-supervised clustering in this image feature space, finally the deep neural network is further fine-tuned. Experiments on the publicly available fundus image dataset DRIVE demonstrate that the proposed method outperforms many other state-of-the-art weakly supervised and unsupervised methods.
Starting Page	1235
Ending Page	1237
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 23, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1893
Journal	Electronics Letters
Publisher Date	2020-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Binary Classify Vessel Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Blood Vessels Computer Vision And Image Processing Technique Deep Neural Network DRIVE Database Eye Feature Extraction Feature Extraction Tool Fundus Image Dataset Haemodynamics Hierarchical Clustering Algorithm Image Enhancement Image Feature Image Feature Space Image Recognition Image Segmentation Learning in AI Medical Image Processing Medical Image Segmentation Neural Computing Technique Neural Nets Nonvessel Pixel Novel Retinal Vessel Segmentation Algorithm Optical And Laser Radiation Patient Diagnostic Method And Instrumentation Pattern Clustering Physiological Optics Physiological Vision Pneumodynamics Pseudogroundtruths Semisupervised Clustering Source Domain Data Subsequent Processing Supervised Retinal Vessel Segmentation Algorithm Target Domain Data Transfer Learning Unsupervised Method
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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