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Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.
TLDR
A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.Abstract:
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. In this paper, we propose 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, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).read more
Citations
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Journal ArticleDOI
Statistical and topological summaries aid disease detection for segmented retinal vascular images
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Patent
Automatic segmentation method of mouse forehead leaf neuron two-photon fluorescence image
Yang Lu,Zhuan Zhou,Zhou Bo,Li Mingli,Wang Lun,Wang Qinglong,Chen Guoqing,Sun Liyuan,Sun Suhua +8 more
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Proceedings ArticleDOI
Detection of the Oocyte Orientation for the ICSI Method Automation
Magdalena Mazur-Milecka,Emilia Kaczmarczyk,Lukasz Wrobel,Patryk Przybylski,Marika Trudnowska,Aleksandra Podwojcik,Monika Jagiello,Krzysztof Lukaszuk,Jacek Ruminski +8 more
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Book ChapterDOI
Deep Learning-Based Framework for Retinal Vasculature Segmentation
Shambhavi Shikha Tiwari,Akash Dholaria,Rajat Pandey,Gauri Nigam,Ranjana Agrawal,Rahee Walambe,Ketan Kotecha +6 more
TL;DR: In this article, a progressive web application is also developed as part of this work, since there is no specific easy-to-use interface to perform segmentation of retinal fundus mentioned in the literature, to the best of our knowledge.
Journal ArticleDOI
Toward Automated Right Ventricle Segmentation via Edge Feature-Induced Self-Attention Multiscale Feature Aggregation Full Convolution Network
Jinping Liu,Mengke Li,Quanquan Gao,Subo Gong,Zhaohui Tang,Yongfang Xie,Ardashir Mohammadzadeh +6 more
TL;DR: In this article , an edge feature-induced self-attention multiscale feature aggregation full convolutional neural network (EFiSaMsFAFUnet) is proposed to address the segmentation tasks.
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