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As bone and air produce weak signals with conventional MR sequences,segmentation of these tissues particularly difficult in MRI. We propose tointegrate patch-based anatomical signatures and an auto-context model into amachine learning framework to iteratively segment MRI into air, bone and softtissue. The proposed semantic classification random forest (SCRF) methodconsists of a training stage and a segmentation stage. During training stage,patch-based anatomical features were extracted from registered MRI-CT trainingimages, and the most informative features were identified to train a series ofclassification forests with auto-context model. During segmentation stage, weextracted selected features from MRI and fed them into the well-trained forestsfor MRI segmentation. The DSC for air, bone and soft tissue obtained withproposed SCRF were 0.976, 0.819 and 0.932, compared to 0.916, 0.673 and 0.830with RF, 0.942, 0.791 and 0.917 with U-Net. SCRF also demonstrated superiorsegmentation performances for sensitivity and specificity over RF and U-Net forall three structure types. The proposed segmentation technique could be auseful tool to segment bone, air and soft tissue, and have the potential to beapplied to attenuation correction of PET/MRI system, MRI-only radiationtreatment planning and MR-guided focused ultrasound surgery.

Air, bone and soft-tissue Segmentation on 3D brain MRI Using Semantic Classification Random Forest with Auto-Context Model