In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances.
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data.
In this work, we propose a new framework for supervised segmentation approaches that is robust to contrast differences between the training MR image and the input image.
In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI.
Accurate PET image reconstruction requires attenuation correction, which is based on the electron density of tissues and can be obtained from CT images.
The proposed CNN automatically identifies the MR contrast of an input brain image volume.
In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images.