Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database

Author(s):  
Ponnada A. Narayana ◽  
Ivan Coronado ◽  
Melvin Robinson ◽  
Sheeba J. Sujit ◽  
Sushmita Datta ◽  
...  
NeuroImage ◽  
2012 ◽  
Vol 59 (4) ◽  
pp. 3774-3783 ◽  
Author(s):  
Paul Schmidt ◽  
Christian Gaser ◽  
Milan Arsic ◽  
Dorothea Buck ◽  
Annette Förschler ◽  
...  

Author(s):  
Cheng‐Chih Hsiao ◽  
Nina L. Fransen ◽  
Aletta M.R. den Bosch ◽  
Kim I.M. Brandwijk ◽  
Inge Huitinga ◽  
...  

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.


2017 ◽  
Vol 134 (3) ◽  
pp. 383-401 ◽  
Author(s):  
Gijsbert P. van Nierop ◽  
Marvin M. van Luijn ◽  
Samira S. Michels ◽  
Marie-Jose Melief ◽  
Malou Janssen ◽  
...  

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