Automated Measurement of Organ Volume Using Ultrasonic Bubble Filling Method

本研究的目的是准确、自动地从超声图像中测量靶器官的体积。为了准确测量容积，必须事先确定靶器官的边界。然而，由于超声图像质量严重恶化，很难令人满意地检测到器官边界。因此，我们开发了一种使用新提出的气泡填充法准确自动测量靶器官体积的方法。将该方法应用于琼脂凝胶幻影数据，可以准确、自动地测量体积。在本研究期间，我们测量了50个胎儿膀胱体积来验证该方法的有效性。数据采集：在扫描过程中，固定探针对准母亲腹部的角度（平行）和速度。然后将该方法应用于数据并测量体积。因此，可以准确和自动地测量体积。最后，我们提出了一种从超声图像中准确、方便地测量膀胱体积的方法。通过平行扫描获得一组二维横切面图像和一组垂直横切面图像，无需特殊硬件即可准确测量膀胱体积。将该方法应用于实验数据和临床数据，可以测量膀胱的体积。此外，我们还构建了能够处理和保存大量临床数据的计算机集。

The purpose of this study is to measure the volume of the target organ accurately and automatically from ultrasound images. In order to measure the volume accurately, it is necessary to determine the boundary of the target organ beforehand. It is, however, difficult to detect the organ boundary satisfactorily, because the ultrasound image quality is seriously deteriorative. Therefore, we developed the method for measuring the volume of the target organ accurately and automatically using the newly proposed Bubble Filling Method. As a result of applying this method to agar gel phantom data, it was possible to measure the volume accurately and automatically.In this research period, we measured 50 fetal bladder volumes to verify the method's effectiveness. Data acquision was as follows : the angle (parallel) and the speed of the probe applied to the mothers' abdomen were fixed during the scanning. Then we applied this method to the data and measured the volume. As a result, it was possible to measure the volume accuraterly and automatically.Finally, we proposed the method for measuring the volume of urinary bladders accurately and easily from ultrasound images. A set of two-dimensional cross-section images and a vertical-section image are acquired by parallel scanning and then the urine bladder volume is measured accurately without special hardware. As a result of applying this method to experimental data and clinical data, it was possible to measure the volume of the urinary bladder.In addition, we constructed computer-set that is able to handle and save enormous clinical data.