Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to noise, motion, and partial volume effects, automated segmentation of lesions from MRI is still a challenging task. In this paper, we propose a two-stage supervised learning framework for automatic brain lesion segmentation. Specifically, in the first stage, intensity-based statistical features, template-based asymmetric features, and GMM-based tissue probability maps are used to train the initial random forest classifier. Next, the dense conditional random field optimizes the probability maps from the initial random forest classifier and derives the whole tumor regions referred as the region of interest (ROI). In the second stage, the optimized probability maps are further intergraded with features from the intensity-based statistical features and template-based asymmetric features to train subsequent random forest, focusing on classifying voxels within the ROI. The output probability maps will be also optimized by the dense conditional random fields, and further used to iteratively train a cascade of random forests. Through hierarchical learning of the cascaded random forests and dense conditional random fields, the multimodal local and global appearance information is integrated with the contextual information, and the output probability maps are improved layer by layer to finally obtain optimal segmentation results. We evaluated the proposed method on the publicly available brain tumor datasets BRATS 2015 & BRATS 2018, as well as the ischemic stroke dataset ISLES 2015. The results have shown that our framework achieves competitive performance compared to the state-of-the-art brain lesion segmentation methods. In addition, contralateral difference and skewness were identified as the important features in the brain tumor and ischemic stroke segmentation tasks, which conforms to the knowledge and experience of medical experts, further reflecting the reliability and interpretability of our framework.
Copyright © 2020. Published by Elsevier Inc.