School of Future Science and Engineering, Soochow University, Suzhou, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
J Digit Imaging. 2023 Jun;36(3):947-963. doi: 10.1007/s10278-023-00783-3. Epub 2023 Feb 2.
Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics.
准确的前列腺超声图像分割对于前列腺癌的临床诊断和图像引导的前列腺手术至关重要。然而，由于超声图像的信噪比低、前列腺与邻近组织之间的对比度低以及前列腺边界的弥散或不可见，准确地分割前列腺具有挑战性。在本文中，我们开发了一种新的混合方法，用于从一系列数据集分割前列腺超声图像，从而生成前列腺的精确轮廓。我们的方法涉及三个关键步骤：（1）应用基于主曲线的方法，获得包含数据坐标及其相应投影索引的数据序列；（2）使用投影索引作为基于分数阶的神经网络的训练输入，以提高结果的准确性；（3）生成平滑的数学映射（通过神经网络的参数表示），提供平滑的前列腺边界，这是神经网络的输出（即优化顶点），并与地面真实轮廓匹配。在多个机构生成的前列腺超声图像数据集上对我们的方法和其他几种最先进的分割方法进行的实验评估表明，我们的方法表现出最好的能力。此外，我们的方法具有鲁棒性，因为它可以基于各种评估指标应用于分割来自多个机构的前列腺超声图像。
