A 3D Fully Convolutional Network Based Semantic Segmentation for Ear Computed Tomography Images

2018 ◽  
Author(s):  
Zhaopeng Gong ◽  
Xiaoguang Li ◽  
Li Zhou ◽  
Hui Zhang
Keyword(s):  
Computed Tomography ◽  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Computed Tomography Images

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 Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

2021 ◽  
Vol 2099 (1) ◽  
pp. 012021
Author(s):  
A V Dobshik ◽  
A A Tulupov ◽  
V B Berikov
Keyword(s):  
Computed Tomography ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Training Data ◽  
Neural Network Architecture ◽  
Brain Images ◽  
Tomographic Images ◽  
Computed Tomography Images ◽  
Segmentation Accuracy ◽  
Weakly Supervised

Abstract This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke in the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architecture with several modifications. Experiments on real computed tomography scans show that the proposed methods increase the segmentation accuracy.

scholarly journals Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

2021 ◽  
Vol 68 (2) ◽  
pp. 2451-2467
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Muhammad Almas Anjum ◽  
Yunyoung Nam ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Computed Tomography Images

Computed Tomography Brain Images Semantic Segmentation

2017 ◽  
pp. 77-104
Author(s):  
Poonam Fauzdar ◽  
Sarvesh Kumar
Keyword(s):  
Computed Tomography ◽  
Positive Predictive Value ◽  
Euclidean Distance ◽  
Global Analysis ◽  
Semantic Segmentation ◽  
Brain Images ◽  
Minimum Euclidean Distance ◽  
Computed Tomography Images ◽  
Multi Level ◽  
Reduced Time

In this paper we applianced an approach for segmenting brain tumour regions in a computed tomography images by proposing a multi-level fuzzy technique with quantization and minimum computed Euclidean distance applied to morphologically divided skull part. Since the edges identified with closed contours and further improved by adding minimum Euclidean distance, that is why the numerous results that are analyzed are very assuring and algorithm poses following advantages like less cost, global analysis of image, reduced time, more specificity and positive predictive value.

scholarly journals U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 82867-82877 ◽  
Author(s):  
Shuchao Chen ◽  
Han Yang ◽  
Jiawen Fu ◽  
Weijian Mei ◽  
Shuai Ren ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Esophageal Cancer ◽  
Semantic Segmentation ◽  
Computed Tomography Images

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 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
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