scholarly journals SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2017 ◽  
Vol 39 (12) ◽  
pp. 2481-2495 ◽  
Author(s):  
Vijay Badrinarayanan ◽  
Alex Kendall ◽  
Roberto Cipolla
Keyword(s):  
Image Segmentation ◽  
Decoder Architecture ◽  
Convolutional Encoder

SegNetRes-CRF: A Deep Convolutional Encoder-Decoder Architecture for Semantic Image Segmentation

2018 ◽  
Author(s):  
Luiz Antonio de Oliveira Junior ◽  
Heitor R. Medeiros ◽  
David Macedo ◽  
Cleber Zanchettin ◽  
Adriano L. I. Oliveira ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Semantic Image Segmentation ◽  
Decoder Architecture ◽  
Convolutional Encoder

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5630
Author(s):  
Chin Yii Eu ◽  
Tong Boon Tang ◽  
Cheng-Hung Lin ◽  
Lok Hua Lee ◽  
Cheng-Kai Lu
Keyword(s):  
Colorectal Cancer ◽  
Automatic Segmentation ◽  
Hospital Setting ◽  
Decoder Architecture ◽  
The Future ◽  
Sensitivity Specificity ◽  
Convolutional Encoder ◽  
Cancer Côlon ◽  
Aided Diagnosis ◽  
Better Than

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

scholarly journals Neural networks for topology optimization

2019 ◽  
Vol 34 (4) ◽  
pp. 215-223 ◽  
Author(s):  
Ivan Sosnovik ◽  
Ivan Oseledets
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
High Performance ◽  
Current Method ◽  
Optimization Methods ◽  
Proposed Model ◽  
Significant Acceleration ◽  
Decoder Architecture ◽  
Segmentation Task ◽  
Convolutional Encoder

Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

scholarly journals A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters

Solar Energy ◽  
2021 ◽  
Vol 223 ◽  
pp. 217-228
Author(s):  
Amirmohammad Moradi Sizkouhi ◽  
Mohammadreza Aghaei ◽  
Sayyed Majid Esmailifar
Keyword(s):  
Fault Detection ◽  
Decoder Architecture ◽  
Convolutional Encoder

scholarly journals Dense Transformer Networks for Brain Electron Microscopy Image Segmentation

2019 ◽  
Author(s):  
Jun Li ◽  
Yongjun Chen ◽  
Lei Cai ◽  
Ian Davidson ◽  
Shuiwang Ji
Keyword(s):  
Electron Microscopy ◽  
Image Segmentation ◽  
Deep Learning ◽  
Network Architecture ◽  
Superior Performance ◽  
Microscopy Image ◽  
Decoder Architecture ◽  
Technical Solutions ◽  
Entire Network ◽  
Biological Image

The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.

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