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首页> 外文期刊>NeuroImage >Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.
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Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

机译：结合标签传播和决策融合的自动解剖脑MRI分割。

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摘要

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.
机译：可以使用用于手动分割和标记结构的协议对三维磁共振（MR）脑图像中的区域进行分类。对于大型队列，时间和专业知识要求使得此方法不切实际。为了实现自动化，可以使用将地图集图像与目标图像相关的解剖对应估计将个体分割传播到另一个个体。最终目标标记的准确性受到限制，但可以通过使用决策融合组合多个细分来潜在地提高。我们研究了30块正常脑部MR图像的分割传播和决策融合，这些图像已手动分割为67个结构。通过使用自由形式变形的非刚性配准来建立对应估计。直接标签传播和间接方法都经过测试。个别传播显示相对于手动细分的平均相似度指数（SI）为0.754 +/- 0.016。使用29个输入细分的决策融合将SI提升至0.836 +/- 0.009。对于通过27个中间图像间接传播单个来源，SI为0.779 +/- 0.013。我们还使用合成输入数据的数值模拟研究了决策融合过程的效果。结果有助于建立一个模型，该模型可根据合并的各个传播分割的数量预测融合的脑分割的质量改善。我们演示了一个可行的过程，该过程超越了以前的自动方法的准确性，并且可以与手动描述竞争。

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