标题： 基于语义分类随机森林与自动上下文模型的三维脑部MRI图像空气、骨组织和软组织分割 显示英文标题

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

摘要： 由于骨和空气在传统MRI序列中产生弱信号，因此在MRI中对这些组织进行分割尤其困难。我们提出将基于补丁的解剖特征和自动上下文模型整合到机器学习框架中，以迭代方式将MRI分割为空气、骨和软组织。所提出的语义分类随机森林（SCRF）方法包括训练阶段和分割阶段。在训练阶段，从配准的MRI-CT训练图像中提取基于补丁的解剖特征，并确定最具有信息量的特征来训练一系列带有自动上下文模型的分类森林。在分割阶段，我们从MRI中提取选定特征并将其输入到训练良好的森林中进行MRI分割。使用所提出的SCRF方法获得的空气、骨和软组织的DSC分别为0.976、0.819和0.932，与RF相比为0.916、0.673和0.830，与U-Net相比为0.942、0.791和0.917。SCRF在所有三种结构类型的灵敏度和特异性方面也表现出优于RF和U-Net的分割性能。所提出的分割技术可以作为分割骨、空气和软组织的有用工具，并有可能应用于PET/MRI系统的衰减校正、仅MRI的放射治疗计划制定和MR引导的聚焦超声手术。 显示英文摘要

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