Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Department of Medical Technology, Jiangsu Province Hospital, Nanjing, Jiangsu, China.
Comput Methods Programs Biomed. 2022 Jun;219:106752. doi: 10.1016/j.cmpb.2022.106752. Epub 2022 Mar 17.
Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for image-guided prostate interventions and prostate cancer diagnosis. However, it remains a challenging task for various reasons, including a missing or ambiguous boundary between the prostate and surrounding tissues, the presence of shadow artifacts, intra-prostate intensity heterogeneity, and anatomical variations.
Here, we present a hybrid method for prostate segmentation (H-ProSeg) in TRUS images, using a small number of radiologist-defined seed points as the prior points. This method consists of three subnetworks. The first subnetwork uses an improved principal curve-based model to obtain data sequences consisting of seed points and their corresponding projection index. The second subnetwork uses an improved differential evolution-based artificial neural network for training to decrease the model error. The third subnetwork uses the parameters of the artificial neural network to explain the smooth mathematical description of the prostate contour. The performance of the H-ProSeg method was assessed in 55 brachytherapy patients using Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) values.
The H-ProSeg method achieved excellent segmentation accuracy, with DSC, Ω, and ACC values of 95.8%, 94.3%, and 95.4%, respectively. Meanwhile, the DSC, Ω, and ACC values of the proposed method were as high as 93.3%, 91.9%, and 93%, respectively, due to the influence of Gaussian noise (standard deviation of Gaussian function, σ = 50). Although the σ increased from 10 to 50, the DSC, Ω, and ACC values fluctuated by a maximum of approximately 2.5%, demonstrating the excellent robustness of our method.
Here, we present a hybrid method for accurate and robust prostate ultrasound image segmentation. The H-ProSeg method achieved superior performance compared with current state-of-the-art techniques. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. The proposed models have the potential to improve prostate cancer diagnosis and therapeutic outcomes.
在经直肠超声（TRUS）图像中准确、稳健地分割前列腺对于图像引导的前列腺介入和前列腺癌诊断具有重要意义。然而，由于前列腺与周围组织之间的边界缺失或不明确、存在阴影伪影、前列腺内强度异质性和解剖变异等各种原因，这仍然是一项具有挑战性的任务。
本研究提出了一种基于少量放射科医生定义的种子点作为先验点的 TRUS 图像前列腺分割混合方法（H-ProSeg）。该方法由三个子网组成。第一个子网使用改进的基于主曲线的模型获得由种子点及其对应的投影指数组成的数据序列。第二个子网使用改进的基于差分进化的人工神经网络进行训练，以减少模型误差。第三个子网使用人工神经网络的参数来解释前列腺轮廓的平滑数学描述。使用 Dice 相似系数（DSC）、Jaccard 相似系数（Ω）和准确性（ACC）值评估了 H-ProSeg 方法在 55 名近距离放射治疗患者中的性能。
H-ProSeg 方法实现了出色的分割准确性，DSC、Ω 和 ACC 值分别为 95.8%、94.3%和 95.4%。同时，由于高斯噪声的影响（高斯函数的标准差，σ=50），该方法的 DSC、Ω 和 ACC 值高达 93.3%、91.9%和 93%。尽管σ从 10 增加到 50，但 DSC、Ω 和 ACC 值的波动最大约为 2.5%，表明我们的方法具有出色的稳健性。
本研究提出了一种用于准确、稳健的前列腺超声图像分割的混合方法。与当前最先进的技术相比，H-ProSeg 方法具有更好的性能。对前列腺精确边界的了解对于风险结构的保护至关重要。所提出的模型有可能改善前列腺癌的诊断和治疗效果。
