DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images

In neuroimaging, multiple automated methods have been developed for various problems, such as brain extraction, segmentation of anatomical regions of interest (ROIs), white matter lesion (WML) segmentation and segmentation of brain tumor sub-regions. Importantly, each of these problems have their own specific challenges, mainly due to variations in image modalities and imaging signatures that best characterize target regions. These variations motivated development of a large number of distinct task-specific segmentation methods (Kalavathi P, 2016; Anbeek et al., 2004; Eugenio Iglesias and Sabuncu, 2014; Gordillo et al., 2013; Despotovic et al., 2015). Machine learning has played a key role in enabling novel methods that achieved accuracy comparable to, or surpassing human raters. In the commonly used supervised learning framework, examples with ground-truth labels are presented to the learning algorithm in order to construct a model that learns imaging patterns that characterize the target segmentations.