scholarly journals A Digital Pediatric Brain Structure Atlas from T1-Weighted MR Images

2006 ◽  
pp. 332-339 ◽  
Author(s):  
Zuyao Y. Shan ◽  
Carlos Parra ◽  
Qing Ji ◽  
Robert J. Ogg ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images ◽  
Pediatric Brain

scholarly journals A pediatric brain structure atlas from T1-weighted MR images

2006 ◽  
Author(s):  
Zuyao Y. Shan ◽  
Carlos Parra ◽  
Qing Ji ◽  
Robert J. Ogg ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images ◽  
Pediatric Brain

Reliability of brain structure morphometry in hydrocephalic children using MR images

1996 ◽  
Vol 14 (6) ◽  
pp. 649-655 ◽  
Author(s):  
Michael E. Brandt ◽  
Timothy P. Bohan ◽  
Kelly Thorstad ◽  
Steven R. McCauley ◽  
Kevin C. Davidson ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images

scholarly journals Pediatric Brain: No Increased Signal Intensity in the Dentate Nucleus on Unenhanced T1-weighted MR Images after Consecutive Exposure to a Macrocyclic Gadolinium-based Contrast Agent

Radiology ◽  
2017 ◽  
Vol 283 (3) ◽  
pp. 828-836 ◽  
Author(s):  
Alexander Radbruch ◽  
Robert Haase ◽  
Philipp Kickingereder ◽  
Philipp Bäumer ◽  
Sebastian Bickelhaupt ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Signal Intensity ◽  
Dentate Nucleus ◽  
Mr Images ◽  
Pediatric Brain

scholarly journals Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 938
Author(s):  
Takaaki Sugino ◽  
Toshihiro Kawase ◽  
Shinya Onogi ◽  
Taichi Kin ◽  
Nobuhito Saito ◽  
...  
Keyword(s):  
Brain Structure ◽  
Class Imbalance ◽  
Cross Entropy ◽  
Loss Functions ◽  
Mr Images ◽  
Distance Map ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Frequency Weighting ◽  
Class Segmentation

Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively.

scholarly journals Pediatric brain extraction from T2-weighted MR images using 3D dual frame U-net and human connectome database

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S523
Author(s):  
Dongchan Kim ◽  
Jong-Hee Chae ◽  
Sunkyue Kim ◽  
Yeji Han
Keyword(s):  
Dual Frame ◽  
Mr Images ◽  
Brain Extraction ◽  
Pediatric Brain

Special, radical, failure of reduction in psychiatry

2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross
Keyword(s):  
Mental Disorder ◽  
Brain Structure ◽  
Causal Structure ◽  
Network Models ◽  
Gambling Addiction ◽  
Brain Disorder ◽  
Mental Processes ◽  
Special Radical ◽  
Intentional Relationships

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

Our understanding of neural codes rests on Shannon's foundations

2019 ◽  
Vol 42 ◽  
Author(s):  
Charles R. Gallistel
Keyword(s):  
Brain Structure ◽  
Neural Tissue ◽  
Neural Codes ◽  
Flow Of Information

Abstract Shannon's theory lays the foundation for understanding the flow of information from world into brain: There must be a set of possible messages. Brain structure determines what they are. Many messages convey quantitative facts (distances, directions, durations, etc.). It is impossible to consider how neural tissue processes these numbers without first considering how it encodes them.

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