Peng Zhao, Shan Hongming, Yang Xiaoyu, Li Shuzhou, Tang Du, Cao Ying, Shao Qigang, Huo Wanli, Yang Zhen
Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Med Phys. 2024 Feb;51(2):1277-1288. doi: 10.1002/mp.16638. Epub 2023 Jul 24.
Accurate measurement of bladder volume is necessary to maintain the consistency of the patient's anatomy in radiation therapy for pelvic tumors. As the diversity of the bladder shape, traditional methods for bladder volume measurement from 2D ultrasound have been found to produce inaccurate results.
To improve the accuracy of bladder volume measurement from 2D ultrasound images for patients with pelvic tumors.
The bladder ultrasound images from 130 patients with pelvic cancer were collected retrospectively. All data were split into a training set (80 patients), a validation set (20 patients), and a test set (30 patients). A total of 12 transabdominal ultrasound images for one patient were captured by automatically rotating the ultrasonic probe with an angle step of 15°. An incomplete 3D ultrasound volume was synthesized by arranging these 2D ultrasound images in 3D space according to the acquisition angles. With this as input, a weakly supervised learning-based 3D bladder reconstruction neural network model was built to predict the complete 3D bladder. The key point is that we designed a novel loss function, including the supervised loss of bladder segmentation in the ultrasound images at known angles and the compactness loss of the 3D bladder. Bladder volume was calculated by counting the number of voxels belonging to the 3D bladder. The dice similarity coefficient (DSC) was used to evaluate the accuracy of bladder segmentation, and the relative standard deviation (RSD) was used to evaluate the calculation accuracy of bladder volume with that of computed tomography (CT) images as the gold standard.
The results showed that the mean DSC was up to 0.94 and the mean absolute RSD can be reduced to 6.3% when using 12 ultrasound images of one patient. Further, the mean DSC also was up to 0.90 and the mean absolute RSD can be reduced to 9.0% even if only two ultrasound images were used (i.e., the angle step is 90°). Compared with the commercial algorithm in bladder scanners, which has a mean absolute RSD of 13.6%, our proposed method showed a considerably huge improvement.
The proposed weakly supervised learning-based 3D bladder reconstruction method can greatly improve the accuracy of bladder volume measurement. It has great potential to be used in bladder volume measurement devices in the future.
在盆腔肿瘤放射治疗中，准确测量膀胱体积对于保持患者解剖结构的一致性至关重要。由于膀胱形状的多样性，已发现传统的二维超声膀胱体积测量方法会产生不准确的结果。
提高盆腔肿瘤患者二维超声图像膀胱体积测量的准确性。
回顾性收集130例盆腔癌患者的膀胱超声图像。所有数据被分为训练集（80例患者）、验证集（20例患者）和测试集（30例患者）。通过以15°的角度步长自动旋转超声探头，为一名患者总共采集12幅经腹超声图像。通过根据采集角度在三维空间中排列这些二维超声图像，合成一个不完整的三维超声体积。以此为输入，构建一个基于弱监督学习的三维膀胱重建神经网络模型，以预测完整的三维膀胱。关键在于我们设计了一种新颖的损失函数，包括已知角度超声图像中膀胱分割的监督损失和三维膀胱的紧凑性损失。通过计算属于三维膀胱的体素数量来计算膀胱体积。采用骰子相似系数（DSC）评估膀胱分割的准确性，以计算机断层扫描（CT）图像的膀胱体积计算准确性作为金标准，采用相对标准偏差（RSD）评估膀胱体积的计算准确性。
结果表明，使用一名患者的12幅超声图像时，平均DSC高达0.94，平均绝对RSD可降至6.3%。此外，即使仅使用两幅超声图像（即角度步长为90°），平均DSC也高达0.90，平均绝对RSD可降至9.0%。与膀胱扫描仪中的商业算法相比，其平均绝对RSD为13.6%，我们提出的方法显示出显著的改进。
所提出的基于弱监督学习的三维膀胱重建方法可大大提高膀胱体积测量的准确性。未来在膀胱体积测量设备中具有巨大的应用潜力。
