Cascaded convolutional networks for unsupervised brain tissue segmentation and bias field estimation.

Brain tissue segmentation from MR images is a critical step for quantifying the brain morphology in neuroimaging studies. While deep learning (DL) based brain tissue segmentation methods have achieved promising performance, most of them are built upon supervised learning and therefore their performance is bounded by the training data used and limited by the small size of datasets with manual segmentation labels. To leverage the large amount of unlabeled brain imaging data, we develop an unsupervised DL model for joint brain tissue segmentation and bias field estimation using cascaded convolutional networks. The proposed DL model consists of multiple cascaded bias field estimation modules and one segmentation module. The bias field estimation modules are applied to the input image for estimating the bias field and generating a bias-free image recursively, and the bias field corrected image is then fed into the segmentation module to obtain the brain tissue segmentation result. A Gaussian mixture model is adopted to characterize the bias-free image with tissue-specific intensity statistics and the model fitting error is adopted as the loss function to guide the optimization of the model parameters progressively in an unsupervised setting. We have evaluated the proposed method on the HCP-Aging and HCP-Development datasets. Quantitative results have demonstrated that our unsupervised DL model could obtain competitive bias field correction and segmentation performance, compared with state-of-the-art bias field correction methods and unsupervised segmentation methods.