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Adaptive two-way sweeping method to 3D kidney reconstruction

机译:3D肾改造的自适应双向扫描方法

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Objective: This article presents a novel method of automatic kidney contour detection in computed tomography angiography images. This technique allows to read as input the entire set of CTA images. It allows to read an entire set of kidney CTA images as input and then automatically generates binary images of the detected kidney outlines for each scan separately. Its additional feature is a real-time 3D kidney model reconstruction.Methods: The main idea is based on an innovative two-way scanning technique. To adapt an algorithm, a CT is analyzed on the basis of a previous slice. The final kidney contour recognition uses the following digital image processing techniques: mathematical morphology, region growth, colorization.Results: to assess the quality of our technique, we consulted the results with a pathology department. The F1 score of the researched method is 88 % compared to human specialist's verification. We also conducted a comparative study of computation time, system reliability, and recognition accuracy using three recent alternative methods.Conclusion: In comparison to machine learning algorithms, the presented method is very precise thanks to the application of the adaptive sweeping technique. This solution can be successfully applied in CTA image analyzing, visualization, and neoplastic changes detection. Significance: computer-aided medical diagnostic is currently one of the greatest challenges for biomedical engineers. The technique can find a real-life application in medical centers and medical-pathology departments.
机译:目的:本文提出了一种在计算机断层造影图像中自动肾脏轮廓检测的新方法。该技术允许读取整组CTA图像的输入。它允许将整组肾脏CTA图像作为输入读取,然后自动为每个扫描分别生成检测到的肾内轮廓的二进制图像。其附加功能是一个实时3D肾模型重建。方法:主要思想是基于创新的双向扫描技术。为了适应算法,基于先前的切片分析CT。最终的肾脏轮廓识别使用以下数字图像处理技术:数学形态,区域生长,颜色。结果:要评估我们技术的质量,我们将结果与病理部门咨询。与人类专家的验证相比,研究方法的F1评分为88%。我们还使用三种替代方法进行了对计算时间,系统可靠性和识别准确性的比较研究。结论:与机器学习算法相比,由于适应性扫描技术的应用,所提出的方法非常精确。该解决方案可以成功应用于CTA图像分析,可视化和肿瘤变化检测。意义:计算机辅助医疗诊断目前是生物医学工程师最大的挑战之一。该技术可以在医疗中心和医学病理部门找到真实寿命。

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