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RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields
Publication date: Available online 11 February 2020Source: NeuroImageAuthor(s): Gaoxiang Chen, Qun Li, Fuqian Shi, Islem Rekik, Li Wang, Zhifang PanSegmentation 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-...
Source: NeuroImage - February 11, 2020 Category: Neuroscience Source Type: research
