Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Comput Methods Programs Biomed. 2014 Apr;114(1):11-28. doi: 10.1016/j.cmpb.2013.12.022. Epub 2014 Jan 16.
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.
尽管磁共振成像（MRI）相对于其他影像学技术（如计算机断层扫描（CT））具有优势，但在 MRI 中进行肝脏分割几乎没有完全自动化的方法。受医疗需求的推动，已经进行了 MRI 中的肝脏分割。为此，我们提出了一种新的基于分水岭变换和随机分区的肝脏分割方法。使用标记控制算法减少经典分水岭过分割。为了提高所选轮廓的准确性，通过应用随机分水岭的新变体成功增强了原始图像的梯度。此外，为了获得最终的肝脏掩模，执行最终分类器。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 MRI 中自动分割肝脏的强大工具。
