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Neural ODEs for Image Segmentation with Level Sets
[article]
2019
arXiv
pre-print
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We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method. ...
In addition, for cases where an initial contour is not available and to alleviate the need for careful choice or design of contour embedding functions, we propose a NODE-based method that evolves an image ...
Nguyen for their invaluable time and advice. ...
arXiv:1912.11683v1
fatcat:kirzdbjev5a4tffy2u7wdmq3nm
Medical Image Segmentation via Unsupervised Convolutional Neural Network
[article]
2020
arXiv
pre-print
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For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. ...
In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. ...
M ode 1
M ode 2
M ode 3
M ode 4
Level Set ACWE
DSC 0.593±0.19 0.661±0.16 0.732±0.12 0.856±0.09
0.518±0.337
Table 1: Proposed Method Level Set ACWE
Time (Sec)
0.006 ± 0.022
2.698±0.085
Table ...
arXiv:2001.10155v4
fatcat:6dfunjgrnvhi3fgcrrqw7lgjm4
Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
[article]
2019
arXiv
pre-print
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Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. ...
A key disadvantage is the hard-coding of the receptive field size, which requires architecture optimization for each segmentation task. ...
Acknowledgments We would like to thank Jasper Linmans for the useful discussions. ...
arXiv:1910.10470v1
fatcat:5u3kgwv5fnax5myntslbeonu64
Performance Evaluation of Optic Disc Segmentation Algorithms in Retinal Fundus Images : an Empirical Investigation
2014
International Journal of Advanced Science and Technology
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These five methods are based on use of algorithms namely; distance regularized level set, Otsu thresholding, region growing, particle Swarm optimization, generalized regression neural network. ...
This work focuses on automatic segmentation of Optic disc from fundus images, which is an important parameter for disease diagnosis. ...
., Martinez-de-la-Casa J.M. for making the public database DRIONS_DB and the ground truth available. We acknowledge the help of Dr. Medha Prabhudesai and Dr. ...
doi:10.14257/ijast.2014.69.03
fatcat:glrksr7ab5e3xkidfgo2e5xdeq
A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assesment in Retinal Color Fundus Images
[article]
2018
arXiv
pre-print
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In this work, we present a Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus images. ...
The cross-testing performance of the proposed method on an independent validation set acquired using a different camera and image resolution was found to be good with an average dice score of 0.92 for ...
on the off-site validation set that has greatly assisted this work. ...
arXiv:1808.01355v1
fatcat:ezt4x2ovgzaybpfbr3jq45jrma
Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network
2022
BioMed Research International
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In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). ...
The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. ...
Prior to doing the image enhancement for OD segmentation, the image is converted to grayscale. ...
doi:10.1155/2022/6799184
pmid:35547359
pmcid:PMC9085314
fatcat:hznrfjal5jh5pfmph3cespufnu
Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations
2021
Computational and Mathematical Methods in Medicine
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The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. ...
model is used for cell image segmentation. ...
We put an ODE-block into a U-Net network model for blood cell image segmentation (named NODEs-UNet). ...
doi:10.1155/2021/5590180
pmid:34413897
pmcid:PMC8369191
fatcat:g5jcqwst6fgsjbwf5njj5li3vi
Advanced Artery / Vein Classification System in Retinal Images for Diabetic Retinopathy
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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images.This paper proposes a graphbased artery vein classification system inretinal images for diabetic retinopathybased on the structural informationextracted from the retinalvasculature. ...
An automatic screening system might facilitate to solve this resource short fall.The retinal vasculature consists of the arteries and veins with their tributaries that are visible at intervals in the retinal ...
The 40 images were divided into atraining set and a testing set. In the testing set images, a second independentmanual segmentation exists as well.
B. ...
doi:10.17762/ijritcc2321-8169.150178
fatcat:lnmhyy6o6fgrfmwli3uclbam7m
REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
[article]
2018
arXiv
pre-print
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Afterwards, both the ground truth and generated images are fed to a convolution neural network (CNN) to extract their multi-level features. ...
The cropped image is then passed to an encoder-decoder network with skip connections also known as generator. ...
In addition, [7] proposed a multi-scale deep model with multi-level loss for segmenting OD and OC regions in fundus images. ...
arXiv:1807.11433v1
fatcat:pbnytkwkbrhqpipd27txyg54uu
Automated Optic Disc Segmentation and Classification Model using Optimal Convolutional Neural Network for Glaucoma Diagnosis System
2019
International Journal of Engineering and Advanced Technology
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This paper presented an automated OD segmentation and classification model for the detection of glaucoma. ...
An important way to diagnose the glaucoma is to detect and segment the optic disc (OD) area. ...
The threshold process resulted to the binary images which contains a set of two gray level images namely black and white. ...
doi:10.35940/ijeat.a1928.109119
fatcat:ept322ndmjdetcazoph6qdh6oq
Retinal Glaucoma Detection Using Deep Learning Algorithm
2022
International Journal of Intelligent Systems and Applications in Engineering
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In this work, a minimum of 10 features such as Mean, Variance, Entropy with the data set trained images on 15 images using NN (neural network) training is implemented, and NN classifier based Normal or ...
This work proposes deep learning-based system for glaucoma diagnosis using retinal fundus images, developed using image processing and deep learning approaches. ...
As the best candidate region for OD, the brightest probable OD region with the highest Vessel-Sum and Solidity is detected. ...
doi:10.18201/ijisae.2022.267
fatcat:vfhetews45a6ln3x3y2r6ewf7e
Deep Convolutional Networks based on encoder-decoder architecture for automatic Optic Disc segmentation in retina images
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Finally, an overview is given of future usage of convolutional neural networks for image segmentation problems in medical imaging contexts and the challenges involved therein. ...
In the scope of this study, we focused on the architectures of convolutional neural networks in particular. We present this architecture, and how it can be used for Image Segmentation. ...
The training set of Optic Disc contains 54 images with mask labels, and the testing set contains 27 images with mask labels. Figure 3 presents sample images and segmentations of Optic Disc. ...
doi:10.30534/ijatcse/2020/181922020
fatcat:g7awy62m3fhf3mloe3rikgxavu
Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
2019
Sensors
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In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. ...
Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. ...
For OD segmentation, W and H are set as 384; for OC segmentation, W and H are set as 256. Each image Xi is required to perform convolutional operations at the initial step. ...
doi:10.3390/s19204401
fatcat:x6obs5ah2rdvvnc6svf4jy4y4y
Glaucoma Detection Using Image Processing and Supervised Learning for Classification
2022
Journal of Healthcare Engineering
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It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. ...
For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. ...
[14] segmented the data for OD and OC by using a threshold technique. e author presents a multilevel thresholding approach for segmenting the image with fuzzy partitions of image histograms and entropy ...
doi:10.1155/2022/2988262
pmid:35273784
pmcid:PMC8904131
fatcat:3evzldvw2jg4lazy6653fhj6yy
W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
[article]
2020
arXiv
pre-print
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We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. ...
To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. ...
CONCLUSION In summary, we sought to develop a deep neural network combined with MTL for simultaneous exudate and OD segmentation. ...
arXiv:2006.06277v1
fatcat:tpieq4god5fejfzwiibcj43yzy
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