Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets for IsoIntense Infant Brain MRI Segmentation

The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks. Infant brain MRI tissue segmentation is a complex deep learning task, where the white matter and gray matter of the developing brain at about 6 months of age show similar T1 and T2 relaxation times, having similar intensity values on both T1 and T2-weighted MRIs. In this paper, we propose deep learning strategies to overcome the challenges of isointense infant brain MRI tissue segmentation. We introduce an exclusive multi-label training method to independently segment the mutually exclusive brain tissues with similarity loss function parameters that are balanced based on class prevalence. Using our training technique based on similarity loss functions and patch prediction fusion we decrease the number of parameters in the network, reduce the complexity of the training process focusing the attention on less number of tasks, while mitigating the effects of data imbalance between labels and inaccuracies near patch borders. By taking advantage of these strategies we were able to perform fast image segmentation, using a network with less parameters than many state-of-the-art networks, being image size independent overcoming issues such as 3D vs 2D training and large vs small patch size selection, while achieving the top performance in segmenting brain tissue among all methods in the 2017 iSeg challenge. We present a 3D FC-DenseNet architecture, an exclusive multilabel patchwise training technique with balanced similarity loss functions and a patch prediction fusion strategy that can be used on new classification and segmentation applications with two or more very similar classes. This strategy improves the training process by reducing its complexity while providing a trained model that works for any size input and is fast and more accurate than many state-of-the-art methods.