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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Liver segmentation in MRI: A fully automatic method based on stochastic partitions.
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Liver segmentation in MRI: A fully automatic method based on stochastic partitions.

机译：MRI中的肝脏分割：一种基于随机分区的全自动方法。

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摘要

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.
机译：尽管与其他放射学技术（例如计算机断层扫描（CT））相比，这种类型的采集具有优势，但在磁共振图像（MRI）中几乎没有全自动的肝分割方法。出于医学要求，已经在MRI中进行了肝脏分割。为此，我们提出了一种基于分水岭变换和随机分区的肝脏分割新方法。使用标记控制算法可减少经典分水岭的过度分割。为了提高所选轮廓的精度，通过应用随机分水岭的新变种成功地增强了原始图像的梯度。此外，执行最终分类器以获得最终肝罩。使用训练数据集调整方法的最佳参数，然后将其应用于其余研究（17个数据集）。与其他方法相比，所获得的结果（Jaccard系数为0.91±0.02）表明，随机分水岭的新变体是在MRI中自动分割肝脏的强大工具。

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