Unsupervised Brain Lesion Segmentation Using Posterior Distributions Learned by Subspace-based Generative Model

Unsupervised brain lesion segmentation, focusing on learning normative distributions from images of healthy subjects, are less dependent on lesion-labeled data, thus exhibiting better generalization capabilities. A fundamental challenge in learning normative distributions of images lies in the high dimensionality if image pixels are treated as correlated random variables to capture spatial dependence. In this study, we proposed a subspace-based deep generative model to learn the posterior normal distributions. Specifically, we used probabilistic subspace models to capture spatial-intensity distributions and spatial-structure distributions of brain images from healthy subjects. These models captured prior spatial-intensity and spatial-structure variations effectively by treating the subspace coefficients as random variables with basis functions being the eigen-images and eigen-density functions learned from the training data. These prior distributions were then converted to posterior distributions, including both the posterior normal and posterior lesion distributions for a given image using the subspace-based generative model and subspace-assisted Bayesian analysis, respectively. Finally, an unsupervised fusion classifier was used to combine the posterior and likelihood features for lesion segmentation. The proposed method has been evaluated on simulated and real lesion data, including tumor, multiple sclerosis, and stroke, demonstrating superior segmentation accuracy and robustness over the state-of-the-art methods. Our proposed method holds promise for enhancing unsupervised brain lesion delineation in clinical applications.