Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features

2005 ◽  
pp. 400-409 ◽  
Author(s):  
Tae Hyung Kim ◽  
Il Kyu Eom ◽  
Yoo Shin Kim
Keyword(s):  
Neural Networks ◽  
Texture Segmentation ◽  
Multi Scale ◽  
Wavelet Features

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

Single Image Dehazing via Multi-scale Convolutional Neural Networks

2016 ◽  
pp. 154-169 ◽  
Author(s):  
Wenqi Ren ◽  
Si Liu ◽  
Hua Zhang ◽  
Jinshan Pan ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Single Image ◽  
Image Dehazing ◽  
Multi Scale ◽  
Single Image Dehazing

Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks

2018 ◽  
Vol 21 (6) ◽  
pp. 1721-1743 ◽  
Author(s):  
Xipeng Pan ◽  
Dengxian Yang ◽  
Lingqiao Li ◽  
Zhenbing Liu ◽  
Huihua Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cell Detection ◽  
Multi Scale ◽  
Fully Convolutional Neural Networks ◽  
Microscopy Images
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