Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks.

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest.

Monitoring acute inflammatory processes in mouse muscle by MR imaging and spectroscopy: a comparison with pathological results.

We have studied an animal model of acute local inflammation in muscle induced by Aspergillus fumigatus by using magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). We have compared our data to those found using histopathology and segmentation maps obtained by the mathematical processing of three-dimensional T2-weighted MRI data via a neural network. The MRI patterns agreed satisfactorily with the clinical and biological evidence of the phases of acute local infection and its evolution towards chronicity.