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U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image
[article]
2020
arXiv
pre-print
Preserved Fulltext
Paying attention to the low contrast small blood vessel in fundus region, first time we proposed to combine graph based smoothing regularizer with the loss function in the U-net framework. ...
The potential of the proposed graph based smoothing regularizer in reconstructing small vessel is compared over the classical U-net with or without regularizer. ...
Conclusions We newly proposed a graph based smoothing regularization term with the loss function in the U-net framework for the segmentation of small vessels in the retinal image. ...
arXiv:2009.07567v1
fatcat:72ty4hmehrh7nk2lijnynpan7e
Deep Guidance Network for Biomedical Image Segmentation
2020
IEEE Access
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Segmentation of 2D images is a fundamental problem for biomedical image analysis. The most widely used architecture for biomedical image segmentation is U-Net. ...
Our method enables end to end training and fast inference (43ms for one image). We conduct extensive experiments for the task of vessel segmentation and optic disc and cup segmentation. ...
Early attempts for OC and OD segmentation in fundus images are based on hand-craft features such as color [27] , contrast thresholding [28] , level set approach [29] , clustering based methods [30] ...
doi:10.1109/access.2020.3002835
fatcat:rv66rxz5dfe3to6pcmqqqvp3bq
Improvement for Single Image Super-resolution and Image Segmentation by Graph Laplacian Regularizer Based on Differences of Neighboring Pixels
2022
International Journal of Intelligent Engineering and Systems
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This study presents a regularization term based on the differences of neighboring pixels. We define the differences of neighboring pixels by using the Graph Theory approach. ...
Qualitative and quantitative results show that our developed regularizer proved to enhance the boundary structure on image super-resolution and image segmentation tasks, which is achieved Structural Similarity ...
U-Net U-Net + GLRDN 0.7625 0.8040 0.9624 0.9326 0.9497 0.9580 0.9350 0.9561 Figure. 2 Comparison results of blood vessel segmentation between our proposed regularizer and without proposed regularizer ...
doi:10.22266/ijies2022.0228.10
fatcat:fyaondf2ojcxjncyqswcoyfbmm
Fully Automated Tree Topology Estimation and Artery-Vein Classification
[article]
2022
arXiv
pre-print
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We present a fully automatic technique for extracting the retinal vascular topology, i.e., how the different vessels are connected to each other, given a single color fundus image. ...
As our experiments show, our topology-based artery-vein labeling achieved state-of-the-art results on multiple datasets. ...
U-Net, a multi-level encoder-decoder based architecture is uses as a backbone for major medical image segmentation task [16] . ...
arXiv:2202.02382v1
fatcat:daqpguvdknaqdh2xgl34nm6bri
Retinal Vessel Segmentation Using Deep Learning: A Review
2021
IEEE Access
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In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. ...
INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network. ...
Compared with traditional CNNs, FCNs can predict each pixel in an image or image patch, so it is more suitable and fast for image segmentation tasks.
C. U-NET Ronneberger, et al. ...
doi:10.1109/access.2021.3102176
fatcat:x3jqvx67qndgnoi7wi357b6zwe
Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets
2021
BioMed Research International
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Using these junctions, we determine regions of the secondary coronary arteries (rectangular regions) for a secondary coronary artery-extracted segment with the second U-net model. ...
To overcome this problem, we propose a novel segmenting method for coronary artery extraction from angiograms based on the primary and secondary coronary artery. ...
frameworks for coronary vessel segmentation and an extended method to deal with 3D images. ...
doi:10.1155/2021/5548517
pmid:33898624
pmcid:PMC8052146
fatcat:jrynubgyojgqlc7trdbhjwnapq
Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning
[article]
2020
IEEE journal of biomedical and health informatics
accepted
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To eliminate the retinal vessel shadows, we propose a pipeline that firstly use anatomical and OCT imaging knowledge to locate the shadows using their projection on the retinal pigment epithelium layer ...
In this paper, we propose to incorporate medical and imaging prior knowledge with deep learning to address these two problems. ...
ACKNOWLEDGEMENT We would like to thank all the reviewers for their insightful comments and concerns which stimulate us to improve our work. ...
doi:10.1109/jbhi.2020.3023144
pmid:32931435
arXiv:2002.04712v2
fatcat:nfcxejpqy5dbdora35prdjobee
Comparative Analysis of Vessel Segmentation Techniques in Retinal Images
2019
IEEE Access
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INDEX TERMS Vessel segmentation, retinal diseases, image segmentation, retinal fundus images, medical imaging. 114866 VOLUME 7, 2019 A. ...
This survey presents a comparative analysis of various machine learning and deep learning-based methods for automated blood vessel segmentation in retinal images. ...
[28] proposed a Dense U-Net based on patch-based learning method for retinal vessel segmentation. ...
doi:10.1109/access.2019.2935912
fatcat:64ycsdgecza4tkq3m5c7qs2r7m
GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment
2021
SN Computer Science
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Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method for glaucoma screening. ...
The proposed patch-based deep network (GlaucoNet) is trained with a large number of preprocessed input image patches, big enough to include the important discriminatory information around each pixel. ...
Sevastopolsky used a modification of U-Net convolutional neural network (CNN) for OD and OC segmentation in retinal images [37] . ...
doi:10.1007/s42979-021-00491-1
fatcat:eeaxifs5izdw7j7mmfgp3xpgsy
Dense Dilated Network with Probability Regularized Walk for Vessel Detection
[article]
2019
arXiv
pre-print
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To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. ...
The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. ...
Many segmentation methods are based on the encoder-decoder structure, such as SegNet [23] and U-Net [22] . ...
arXiv:1910.12010v1
fatcat:zh7y42mwqzbrxmv2yt63ljeiaq
Automatic Retinal Vessel Segmentation via Deeply Supervised and Smoothly Regularized Network
2018
IEEE Access
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In this paper, we propose an automatic retinal vessel segmentation network using deep supervision and smoothness regularization, which integrates holisticallynested edge detector (HED) and global smoothness ...
INDEX TERMS Vessel segmentation, deep learning, medical image analysis, deep supervision, conditional random field. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their helpful suggestions. ...
doi:10.1109/access.2018.2844861
fatcat:nv5totuwevhxpli54suerajzye
Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields
2020
IEEE Access
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In this paper, we are proposing a novel convolutional neural network architecture for the precise segmentation of the OD in fundus images. ...
The localization and segmentation of optic disc (OD) in fundus images is a crucial step in the pipeline for detecting the early onset of retinal diseases, such as macular degeneration, diabetic retinopathy ...
ACKNOWLEDGMENT We thank Kasturba Medical College (KMC), Manipal, India, for providing the fundus image dataset, and TensorFlow Research Cloud (https://www.tensorflow.org/tfrc) for providing the computing ...
doi:10.1109/access.2020.2972318
fatcat:b4k4pvupezgp5lw7zp7iwzaqzy
Deep Neural Architectures for Medical Image Semantic Segmentation: Review
2021
IEEE Access
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The technique combines RetinaNet's one-stage detector with U-Net architecture for image segmentation. ...
DRIVE Digital Retinal Images for Vessel Extraction (DRIVE) dataset is built up to empower comparative investigations on blood vessel segmentation in retinal fundus images. ...
doi:10.1109/access.2021.3086530
fatcat:hacpqwdxybh63j5ygebqszm7qq
Accurate Tumor Segmentation via Octave Convolution Neural Network
2021
Frontiers in Medicine
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In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. ...
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. ...
Many medical image segmentation problems are improved based on U-Net. ...
doi:10.3389/fmed.2021.653913
pmid:34095168
pmcid:PMC8169966
fatcat:bqlgbbnhqvc3blskhgrzha62mm
Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network
2019
IEEE Access
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Most methods based on deep convolutional neural network (DCNN) have small receptive fields, and hence they are unable to capture global context information of larger regions, with difficult to identify ...
The final segmented retina vessels contain more noise with low classification accuracy. Therefore, in this paper, we propose a DCNN structure named as D-Net. ...
In [27] , it used a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models to segmentation retina vessel ...
doi:10.1109/access.2019.2922365
fatcat:462oythl2vb4benidwvrrtdo3m
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