This article describes the automated computed tomography (CT) image processing technique supporting kidney detection. The main goal of the study is a fully automatic generation of a kidney boundary for each slice in the set of slices obtained in the computed tomography examination. This work describes three main tasks in the process of automatic kidney identification: the initial location of the kidneys using the U-Net convolutional neural network, the generation of an accurate kidney boundary using extended maxima transformation, and the application of the slice scanning algorithm supporting the process of generating the result for the next slice, using the result of the previous one. To assess the quality of the proposed technique of medical image analysis, automatic numerical tests were performed. In the test section, we presented numerical results, calculating the F1-score of kidney boundary detection by an automatic system, compared to the kidneys boundaries manually generated by a human expert from a medical center. The influence of the use of U-Net support in the initial detection of the kidney on the final F1-score of generating the kidney outline was also evaluated. The F1-score achieved by the automated system is $84\% \pm 10\%$ for the system without U-Net support and $89\% \pm 9\%$ for the system with U-Net support. Performance tests show that the presented technique can generate the kidney boundary up to 3 times faster than raw U-Net-based network. The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time. The measurable effect of the developed techniques is a practical help for doctors, specialists from medical centers dealing with the analysis and description of medical image data.

中文翻译：

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基于扩展极大值变换的肾脏边界检测算法在计算机断层扫描诊断中的应用

本文介绍了支持肾脏检测的自动计算机断层扫描（CT）图像处理技术。这项研究的主要目的是为在计算机断层扫描检查中获得的一组切片中的每个切片自动生成肾脏边界。这项工作描述了自动肾脏识别过程中的三个主要任务：使用U-Net卷积神经网络对肾脏进行初始定位，使用扩展极大值转换生成准确的肾脏边界以及将切片扫描算法支持使用上一个切片的结果生成下一个切片的结果的过程。为了评估所提出的医学图像分析技术的质量，进行了自动数值测试。在测试部分，我们提供了数值结果，通过自动系统计算了肾脏边界检测的F1得分，与人类专家从医疗中心手动生成的肾脏边界进行了比较。还评估了在最初检测肾脏时使用U-Net支持对生成肾脏轮廓的最终F1分数的影响。自动化系统获得的F1分数是$84％ \pm 10％$ 对于没有U-Net支持的系统 $89％ \pm 9％$用于具有U-Net支持的系统。性能测试表明，所提出的技术可以比原始的基于U-Net的网络更快地生成肾脏边界3倍。所提出的肾脏识别系统可以成功地用于需要非常快速的图像处理时间的系统中。所开发技术的可测量效果为医生，医学中心专家处理医学图像数据的分析和描述提供了实际帮助。