Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.
A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients.
The three probabilistic segmentations in the three views are linearly fused and thresholded to produce a final brain mask.
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned.
The results showed that proposed approach is suitable to be used for brain ROI detection from DSC perfusion MR images of a human head with abnormal brain anatomy and can, therefore, be applied in the DSC perfusion data analysis.
Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a difficult task due to different types of brain abnormalities.
In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition.
Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols.
To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns.