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首页> 外文期刊>European Journal of Soil Biology >A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images
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A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images

机译:半自动和自动分割算法,用于从活猪CT图像中移除内脏器官

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Removal of internal organs such as lungs, liver, and kidneys is a key step required to compute the lean meat percentage from Computed Tomography (CT) scans of live animals. In this paper, we propose two segmentation techniques to remove these organs focusing on pigs. The first method is semiautomatic, and it starts with the first CT slice and a manually defined mask with internal organs. Then, it applies a four-step iterative process that computes the masks of the next CF slices by using the information of the previous one. To find the best boundary it uses a Dynamic Programming-based approach. At each iteration the user can check the correctness of the new computed mask. The second method is fully automatic, and segments each slice individually by using distance maps and morphological operators, such as dilation. It is composed of three main steps which detect the pig's torso, pre-classify the voxels in different tissues, and segment the internal organs using the information of such classification. Although it has some parameters, user interaction is not required to obtain the results. The proposed approaches have been tested on CT data sets from 9 pigs, and compared with a manual segmentation. To evaluate the results, the precision, recall, and F-score measures have been used. From our test, we can observe that the performance of both methods is very high according to their average F-score. We also analyse how the accuracy of the results in the semi-automatic approach increases when more user interaction is applied. For the automatic approach, we evaluate the dependence of the results on the algorithm's parameters. If robustness is enough, and high accuracy is not required, the automatic algorithm can be used to segment a whole pig in less than 50 s. However, if the user wants to control the level of accuracy, the semi-automatic algorithm is preferred. Both methods are useful to reduce the time needed to segment the internal organs of a pig from hours (manual segmentation) to minutes or seconds. (C) 2017 Elsevier B.V. All rights reserved.
机译:去除肺部,肝脏和肾脏等内器官是计算从过上植物(CT)扫描的瘦肉百分比所需的关键步骤。在本文中,我们提出了两种分段技术,以消除专注于猪的这些器官。第一种方法是半自动,它从第一CT切片和带有内脏器官的手动定义的掩模开始。然后,它应用一项四步迭代过程,它通过使用前一个的信息计算下一个CF片的掩码。要找到最佳边界,它使用基于动态编程的方法。在每次迭代时,用户可以检查新计算蒙版的正确性。第二种方法是全自动的,并且通过使用距离图和形态算子(例如扩张)单独分别分别分段。它由三个主要步骤组成,该主要步骤检测猪躯干,预先分类不同组织中的体素,并使用这种分类的信息分段内部器官。虽然它具有一些参数,但不需要用户交互来获取结果。已经在9猪的CT数据集上测试了所提出的方法,并与手动分割进行比较。为了评估结果,已经使用了精度,召回和F分度。从我们的测试中,我们可以观察到两种方法的性能都根据其平均f分数非常高。我们还分析了在应用更多用户交互时半自动方法的结果的准确性如何增加。对于自动方法,我们评估结果对算法参数的依赖性。如果鲁棒性足够,并且不需要高精度,则自动算法可用于在小于50秒的时间内进行整个猪。但是,如果用户想要控制精度水平,则优选半自动算法。两种方法可用于减少从小时(手动分割)分段为分钟或秒的猪的内部器官所需的时间。 (c)2017 Elsevier B.v.保留所有权利。

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