Peng Tao, Wu Yiyun, Gu Yidong, Xu Daqiang, Wang Caishan, Li Quan, Cai Jing
School of Future Science and Engineering, Soochow University, Suzhou, China.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Front Physiol. 2023 Aug 22;14:1177351. doi: 10.3389/fphys.2023.1177351. eCollection 2023.
Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.
超声图像中的精确轮廓提取对于图像引导的器官干预和疾病诊断具有重要意义。然而，由于器官（即前列腺和肾脏）与周围组织之间的轮廓缺失或模糊、阴影伪影的出现以及器官形状的巨大变异性，这仍然是一个有问题的问题。为了解决这些问题，我们设计了一种包括四个阶段的方法。在第一阶段，使用改进的自适应选择主曲线方法获取数据序列，其中采用有限数量的放射科医生定义的数据点作为先验。第二阶段然后使用增强的量子进化网络来帮助获取最优神经网络。第三阶段涉及在训练神经网络后提高实验结果的精度，同时将数据序列作为输入。在最后阶段，使用由神经网络的模型参数解释的可解释数学公式对轮廓进行平滑处理。我们的实验表明，我们的方法优于其他当前方法，包括基于混合和Transformer的深度学习方法，平均Dice相似系数、Jaccard相似系数和准确率分别达到95.7±2.4%、94.6±2.6%和95.3±2.6%。这项工作开发了一种超声图像智能轮廓提取方法。与最近的最先进方法相比，我们的方法获得了更令人满意的结果。器官精确边界的知识对于风险结构的保护具有重要意义。我们开发的方法有可能提高疾病诊断和治疗效果。
