Abstract:
Automatic accurate segmentation of medical images has significant role in computer-aided diagnosis and disease treatment. The segmentation of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues plays an important role in infant brain structure for studying early brain development. However, this task is very challenging due to low contrast between GM and WM in isointense phase (approximately 6-8 months of age). In this study, we develop a hyper-densely connected convolutional neural network (CNN) for segmentation of volumetric infant brain. The proposed model provides dense connection between layers to improve the performance of flow information in the network. It also allows the multiscale contextual information by concatenating the feature maps of early, intermediate, and later layers. This architecture employs MR-T1 and T2 as input, which are processed in two separate independent paths, and then their low, intermediate, and high layer features are fused for final segmentation. An important change relative to earlier densely connected networks is the application of direct layer connections from the same and different paths. In this scenario, each modality is processed in an independent path, and dense connections occur not only between layers within the same path, but also between layers in different paths. Adopting such dense connectivity leads to benefits of deep supervision and improved gradient flow. Furthermore, by combining the feature maps of early, intermediate, and late convolutional layers, our architecture injects multiscale information into the final segmentation. This suggested approach is examined in the MICCAI Grand Challenge iSEG and obtains significant advantages over existing approaches in terms of parameter efficiency and segmentation accuracy on 6-month infant brain MRI segmentation.
