Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images

2018 ◽  
pp. 201-209 ◽  
Author(s):  
Gabriel Lepetit-Aimon ◽  
Renaud Duval ◽  
Farida Cheriet
Keyword(s):  
Receptive Field ◽  
Semantic Segmentation ◽  
Retinal Vasculature ◽  
Fundus Images ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 673-682
Author(s):  
Jian Ji ◽  
Xiaocong Lu ◽  
Mai Luo ◽  
Minghui Yin ◽  
Qiguang Miao ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodong Huang ◽  
Hui Zhang ◽  
Li Zhuo ◽  
Xiaoguang Li ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Positional Information ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmentation Methods ◽  
Feature Pyramid

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

scholarly journals Class-Wise Fully Convolutional Network for Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3211
Author(s):  
Tian Tian ◽  
Zhengquan Chu ◽  
Qian Hu ◽  
Li Ma
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Semantic Labeling ◽  
Benchmark Datasets ◽  
Semantic Label

Semantic segmentation is a fundamental task in remote sensing image interpretation, which aims to assign a semantic label for every pixel in the given image. Accurate semantic segmentation is still challenging due to the complex distributions of various ground objects. With the development of deep learning, a series of segmentation networks represented by fully convolutional network (FCN) has made remarkable progress on this problem, but the segmentation accuracy is still far from expectations. This paper focuses on the importance of class-specific features of different land cover objects, and presents a novel end-to-end class-wise processing framework for segmentation. The proposed class-wise FCN (C-FCN) is shaped in the form of an encoder-decoder structure with skip-connections, in which the encoder is shared to produce general features for all categories and the decoder is class-wise to process class-specific features. To be detailed, class-wise transition (CT), class-wise up-sampling (CU), class-wise supervision (CS), and class-wise classification (CC) modules are designed to achieve the class-wise transfer, recover the resolution of class-wise feature maps, bridge the encoder and modified decoder, and implement class-wise classifications, respectively. Class-wise and group convolutions are adopted in the architecture with regard to the control of parameter numbers. The method is tested on the public ISPRS 2D semantic labeling benchmark datasets. Experimental results show that the proposed C-FCN significantly improves the segmentation performances compared with many state-of-the-art FCN-based networks, revealing its potentials on accurate segmentation of complex remote sensing images.

scholarly journals A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation

2019 ◽  
Vol 646 ◽  
pp. 012025
Author(s):  
Shuqiang Wang ◽  
Wei Liang ◽  
Hongfei Wang ◽  
Zhuo Chen ◽  
Yiqian Lu
Keyword(s):  
Distal Radius ◽  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals PFCN: a fully convolutional network for point cloud semantic segmentation

2019 ◽  
Vol 55 (20) ◽  
pp. 1088-1090
Author(s):  
Jian Lu ◽  
Tong Liu ◽  
Maoxin Luo ◽  
Haozhe Cheng ◽  
Kaibing Zhang
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals A Semantic Segmentation Method for Buffer Layer Defect Detection in High Voltage Cable

2021 ◽  
Vol 233 ◽  
pp. 01032
Author(s):  
Zhang Jun ◽  
Duan Xiaoli ◽  
Xie Yi ◽  
Duan Jianjia ◽  
Huang Fuyong ◽  
...  
Keyword(s):  
Buffer Layer ◽  
High Voltage ◽  
Defect Detection ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Convolutional Network ◽  
Test Set ◽  
Fully Convolutional Network ◽  
X Ray ◽  
Layer Defect

A semantic segmentation method based on the fully convolutional network is proposed to detect the buffer layer defect in high voltage cable automatically. One hundred seventy-seven high-resolution X-ray images of cables are collected. FCN-8s and VGG16 backbone are adopted. The results indicated that the FCN-8s achieves the mIoU to 0.67 on the test set, proving to be an efficient way to detect the buffer layer defects.

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