School of Future Science and Engineering, Soochow University, Suzhou 215006, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Biomolecules. 2023 Oct 19;13(10):1548. doi: 10.3390/biom13101548.
: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical intervention planning, and follow-up assessments. Kidney US image segmentation consists of extracting useful objects or regions from the total image, which helps determine tissue organization and improve diagnosis. Thus, obtaining accurate kidney segmentation data is an important first step for precisely diagnosing kidney diseases. However, manual delineation of the kidney in US images is complex and tedious in clinical practice. To overcome these challenges, we developed a novel automatic method for US kidney segmentation. : Our method comprises two cascaded steps for US kidney segmentation. The first step utilizes a coarse segmentation procedure based on a deep fusion learning network to roughly segment each input US kidney image. The second step utilizes a refinement procedure to fine-tune the result of the first step by combining an automatic searching polygon tracking method with a machine learning network. In the machine learning network, a suitable and explainable mathematical formula for kidney contours is denoted by basic parameters. : Our method is assessed using 1380 trans-abdominal US kidney images obtained from 115 patients. Based on comprehensive comparisons of different noise levels, our method achieves accurate and robust results for kidney segmentation. We use ablation experiments to assess the significance of each component of the method. Compared with state-of-the-art methods, the evaluation metrics of our method are significantly higher. The Dice similarity coefficient (DSC) of our method is 94.6 ± 3.4%, which is higher than those of recent deep learning and hybrid algorithms (89.4 ± 7.1% and 93.7 ± 3.8%, respectively). : We develop a coarse-to-refined architecture for the accurate segmentation of US kidney images. It is important to precisely extract kidney contour features because segmentation errors can cause under-dosing of the target or over-dosing of neighboring normal tissues during US-guided brachytherapy. Hence, our method can be used to increase the rigor of kidney US segmentation.
肾脏超声（US）成像是评估肾脏健康的重要成像方式，对于诊断、治疗、手术干预规划和随访评估至关重要。肾脏 US 图像分割包括从总图像中提取有用的对象或区域，这有助于确定组织结构并改善诊断。因此，获得准确的肾脏分割数据是精确诊断肾脏疾病的重要第一步。然而，在临床实践中，手动勾画 US 图像中的肾脏既复杂又繁琐。为了克服这些挑战，我们开发了一种新的自动 US 肾脏分割方法。
我们的方法由两个级联步骤组成，用于 US 肾脏分割。第一步利用基于深度融合学习网络的粗分割过程粗略地分割每个输入的 US 肾脏图像。第二步利用细化过程通过结合自动搜索多边形跟踪方法和机器学习网络来微调第一步的结果。在机器学习网络中，通过基本参数表示肾脏轮廓的合适且可解释的数学公式。
我们的方法使用从 115 名患者获得的 1380 个经腹 US 肾脏图像进行评估。基于不同噪声水平的综合比较，我们的方法对肾脏分割实现了准确而稳健的结果。我们使用消融实验来评估方法的每个组成部分的重要性。与最先进的方法相比，我们的方法的评估指标明显更高。我们的方法的 Dice 相似系数（DSC）为 94.6±3.4%，高于最近的深度学习和混合算法（89.4±7.1%和 93.7±3.8%）。
我们开发了一种用于精确分割 US 肾脏图像的从粗到精的架构。精确提取肾脏轮廓特征很重要，因为分割误差可能导致 US 引导近距离放射治疗中目标剂量不足或邻近正常组织剂量过大。因此，我们的方法可用于提高肾脏 US 分割的严格性。
