Prostate cancer is one of the most common deadly diseases in men worldwide, which is seriously affecting people’s life and health. Reliable and automated segmentation of the prostate gland in MRI data is exceptionally critical for diagnosis and treatment planning of prostate cancer. Although many automated segmentation methods have emerged, including deep learning based approaches, segmentation performance is still poor due to the large variability of image appearance, anisotropic spatial resolution, and imaging interference. This study proposes an automated prostate MRI data segmentation approach using bicubic interpolation with improved 3D V-Net (dubbed 3D PBV-Net). Considering the low-frequency components in the prostate gland, the bicubic interpolation is applied to preprocess the MRI data. On this basis, a 3D PBV-Net is developed to perform prostate MRI data segmentation. To illustrate the effectiveness of our approach, we evaluate the proposed 3D PBV-Net on two clinical prostate MRI data datasets, i.e., PROMISE 12 and TPHOH, with the manual delineations available as the ground truth. Our approach generates promising segmentation results, which have achieved 97.65% and 98.29% of average accuracy, 0.9613 and 0.9765 of Dice metric, 3.120mm and 0.9382mm of Hausdorff distance, and average boundary distance of 1.708, 0.7950 on PROMISE 12 and TPHOH datasets, respectively. Our method has effectively improved the accuracy of automated segmentation of the prostate MRI data and is promising to meet the accuracy requirements for telehealth applications.

前列腺癌是全球男性最常见的致命疾病之一，严重影响人们的生命和健康。 MRI 数据中前列腺的可靠且自动分割对于前列腺癌的诊断和治疗计划至关重要。尽管已经出现了许多自动分割方法，包括基于深度学习的方法，但由于图像外观、各向异性空间分辨率和成像干扰的巨大变化，分割性能仍然很差。本研究提出了一种使用双三次插值和改进的 3D V-Net（称为 3D PBV-Net）的自动化前列腺 MRI 数据分割方法。考虑到前列腺中的低频成分，采用双三次插值法对MRI数据进行预处理。在此基础上，开发了3D PBV-Net来执行前列腺MRI数据分割。为了说明我们方法的有效性，我们在两个临床前列腺 MRI 数据集（即 PROMISE 12 和 TPHOH）上评估了所提出的 3D PBV-Net，并以手动描绘作为基本事实。我们的方法产生了有希望的分割结果，在 PROMISE 12 和 TPHOH 数据集上实现了 97.65% 和 98.29% 的平均准确度、0.9613 和 0.9765 的 Dice 度量、3.120mm 和 0.9382mm 的 Hausdorff 距离以及 1.708 和 0.7950 的平均边界距离，分别。我们的方法有效提高了前列腺 MRI 数据自动分割的准确性，有望满足远程医疗应用的准确性要求。