scholarly journals Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

NeuroImage ◽  
2015 ◽  
Vol 108 ◽  
pp. 214-224 ◽  
Author(s):  
Wenlu Zhang ◽  
Rongjian Li ◽  
Houtao Deng ◽  
Li Wang ◽  
Weili Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Brain Image ◽  
Infant Brain ◽  
Brain Image Segmentation

scholarly journals Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

2020 ◽  
Vol 14 ◽  
Author(s):  
Yang Ding ◽  
Rolando Acosta ◽  
Vicente Enguix ◽  
Sabrina Suffren ◽  
Janosch Ortmann ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Neonatal Brain ◽  
Deep Convolutional Neural Networks ◽  
Brain Image ◽  
Brain Image Segmentation

scholarly journals Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR

2021 ◽  
Vol 66 (4) ◽  
pp. 04NT01
Author(s):  
Ya Gao ◽  
Zaisheng Li ◽  
Cheng Song ◽  
Lei Li ◽  
Mengmeng Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Rat Brain ◽  
Convolutional Neural Networks ◽  
Brain Image ◽  
Brain Image Segmentation

scholarly journals 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation

2019 ◽  
Vol 49 (3) ◽  
pp. 1123-1136 ◽  
Author(s):  
Dong Nie ◽  
Li Wang ◽  
Ehsan Adeli ◽  
Cuijin Lao ◽  
Weili Lin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Image ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Infant Brain ◽  
Brain Image Segmentation

scholarly journals Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Feature Representation ◽  
Segmentation Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

2021 ◽  
Vol 419 ◽  
pp. 108-125
Author(s):  
Yunyun Yang ◽  
Ruicheng Xie ◽  
Wenjing Jia ◽  
Zhaoyang Chen ◽  
Yunna Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Level Set Method ◽  
Convolutional Neural Networks ◽  
Level Set ◽  
Deep Convolutional Neural Networks

scholarly journals Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation

2020 ◽  
Vol 73 ◽  
pp. 102994 ◽  
Author(s):  
Jakub Nalepa ◽  
Marek Antoniak ◽  
Michal Myller ◽  
Pablo Ribalta Lorenzo ◽  
Michal Marcinkiewicz
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Hyperspectral Image ◽  
Deep Convolutional Neural Networks
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