Isointense infant brain MRI segmentation with a dilated convolutional neural network
This addresses the challenge of quantitative brain analysis in infants for medical imaging researchers, but appears incremental as it builds on existing CNN methods for a specific dataset.
The study tackled the problem of segmenting white matter, gray matter, and cerebrospinal fluid in infant brain MRI at 6 months, where contrast is limited, by using a dilated triplanar CNN combined with a non-dilated 3D CNN, achieving results as part of the MICCAI grand challenge.
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.