Multi-target Interactive Neural Network for Automated Segmentation of the Hippocampus in Magnetic Resonance Imaging

The hippocampus has been recognized as an important biomarker for the diagnosis and assessment of neurological diseases. Convenient and accurate automated segmentation of the hippocampus facilitates the analysis of large-scale neuroimaging studies. This work describes a novel technique for hippocampus segmentation in magnetic resonance images, in which interactive neural network (Inter-Net) is based on 3D convolutional operations. Inter-Net achieves the interaction through two aspects: one is the compartments, which builds an exponential ensemble network that integrates numerous short networks together when forward propagation. The other is the pathways, which realizes inter-connection between feature extraction and restoration. In addition, a multi-target architecture is proposed by designing multiple objective functions in terms of evaluation index, information theory, and data distribution. The proposed architecture is validated in fivefold cross-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, where the mean Dice similarity indices of 0.919 (± 0.023) and precision of 0.926 (± 0.032) for the hippocampus segmentation. The running time is approximately 42.1 s from reading the image to outputting the segmentation result in our computer configuration. We compare the experimental results of a variety of methods to prove the effectiveness of the Inter-Net and contrast integrated architectures with different objective functions to illustrate the robustness of the fusion. The proposed framework is general and can be easily extended to numerous tissue segmentation tasks while it is tailored for the hippocampus.