The proposed deep network performs brain parcellation at each scale simultaneously (multi-task), where parcellation at fine scale is under the constraint of large scales. In addition, we also present a new focal region based auxiliary network, which focuses on the brain regions difficult to be ...
Growth of scientific attention on deep learning based brain tumor segmentation.aKeyword frequency map in MICCAI from 2018 to 2020. The size of the keyword is proportional to the frequency of the word. We observe that ‘brain’, ‘tumor’, ‘segmentation’, and ‘deep learning’, have drawn l...
2), with our method producing less noisy spatial parcellation. Our evaluation suggests that the model can learn robust nonlinear low-dimensional features from complex and noisy imaging data, while accurately predicting those features from short transients, even for the highly heterogeneous brain tissue....
The processing pipeline included skull stripping, nonlinear registration, subcortical segmentation, cortical surface reconstruction, and parcellation42,43,44,45. The output of the automated processes was visually inspected for obvious errors. Instead of manually correcting for the errors, 15 subjects were ...
python net_segment inference -c ~/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini 1. 来运行网络 主流程 进入net_segment.py 进入niftynet.main() 获取用户参数 *1 参数更新 更新模型路径 将参数打印出并写入模型路径下的settings_inference.txt ...
python net_segment inference -c ~/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini 来运行网络 主流程 进入net_segment.py 进入niftynet.main() 获取用户参数 *1 参数更新 更新模型路径 将参数打印出并写入模型路径下的settings_inference.txt ...
This study uses a whole-brain parcellation with 268 brain regions of interest (ROIs) Shen et al. (2017); Finn et al. (2015) and averages the time series over each ROI as the input of our deep learning framework. We compute the functional connectivity (FC) between any two ROIs via ...
Brain parcellation from volumetric medical images, especially 3D magnetic resonance (MR) images, is a prerequisite for quantifying the structural volumes. It is of great significance on diagnosis, progression assessment, and treatment of a wide range of neurodegenerative diseases such as dementia and Al...
we combined volumes obtained with different segmentation approaches. The cerebral cortex was segmented using a multimodal parcellation described in Glasser et al. [40], which returns 180 features for each hemisphere. For subcortical regions, we combined the classic set of features from FreeSurfer with...
Using these datasets, we created a comprehensive map of resting-state brain networks and a fine-grained functional cortical parcellation based on resting-state functional connectivity. Furthermore, we developed a deep-learning-based approach to map the population-based functional cortical parcellation onto...