Hsu Wei-Yen, Lu Chih-Cheng, Hsu Yuan-Yu
Department of Information Management.
Advanced Institute of Manufacturing with High-Tech Innovations.
Medicine (Baltimore). 2020 Nov 20;99(47):e23083. doi: 10.1097/MD.0000000000023083.
In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.
在本研究中，我们回顾性分析了医院数据库中经手术确诊且具有肾细胞癌病理特征的肾癌记录。我们通过评估所提出的自适应主动轮廓模型（ACM）的结果来评估肿瘤大小的意义。我们研究的目的是开发一种自适应ACM方法，以测量医院患者计算机断层扫描（CT）上肾癌的影像学大小。本文提出了一套医学图像处理方法，应用医院提供的图像并由医生选择更明显的病例，在首次处理以去除噪声图像后，使用所提出的自适应ACM方法圈出肾癌轮廓。结果表明，实验结果与医学专业手动勾勒的轮廓高度相似。准确率高于99%。我们开发了一种新颖的自适应ACM方法，该方法很好地结合了基于知识的系统来勾勒CT成像中肾癌的大小，以支持临床决策。
