242449 Introduction: This study evaluates the efficacy of deep learning (DL) models in brain MRI segmentation, focusing on their performance against the traditional method. We systematically compare several state-of-the-art DL models for brain parcellation, assessing spatial overlap through the DICE si...
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...
In this paper, we modify a 3D U-net utilizing probability maps to perform accurate ventricle parcellation, even with grossly enlarged ventricles and post-surgery shunt artifacts, from MRIs. Our method achieves a mean dice similarity coefficient (DSC) on whole ventricles for healthy controls of ...
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....
Wu, Zhengwang, et al. "Registration-Free Infant Cortical Surface Parcellation Using Deep Convolutional Neural Networks." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. image.png 2. Landmark-based Deep Multi-task Multi-channel Learning ...
We used a whole-brain parcellation comprising 400 cortical regions of interest (ROIs)52 (Fig. 1B) and 19 subcortical ROIs53 (Fig. 1C). For each participant and each fMRI run, functional connectivity (FC) was computed as Pearson’s correlations between the average time series of each pair...
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 ...
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 ...
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 ...