Thalamus segmentation using convolutional neural networks

Abstract

Thalamus segmentation plays an important role in studies that are related to neural system diseases. Existing thalamus segmentation algorithms use traditional image processing techniques on magnetic resonance images (MRI), which suffer from accuracy and efficiency. In recent years, deep convolutional neural networks (CNN) have been able to outperform many conventional algorithms in medical imaging tasks. We propose segmenting the thalamus using a 3D CNN that takes an MPRAGE image and a set of feature images derived from a diffusion tensor image (DTI). Experimental results demonstrate that using CNNs to segment the thalamus can improve accuracy and efficiency on various datasets.