FRNET: Flattened Residual Network for Infant MRI Skull Stripping
This addresses skull stripping for infant MRI, a domain-specific problem in medical imaging, with incremental improvements over existing methods.
The authors tackled skull stripping in infant MRI, which is challenging due to small brain size and intensity changes, by proposing FRNET, a CNN-based framework with a flattened residual network and boundary loss, achieving better performance than state-of-the-art methods across age groups from newborns to 48-month-olds on a dataset of 343 images.
Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.