中华医学杂志
2021年 · 第101卷第07期
中华医学杂志
医学影像人工智能的现状与未来
基于CT平扫冠状动脉周围脂肪影像组学诊断非钙化斑块的可行性
基于人工智能技术的FFRCT评估左冠状动脉前降支纵深型心肌桥血流动力学变化
基于深度学习胰腺分割算法在双期CT上提升胰腺临界区域分割能力分析
基于深度学习的肺结节分类分割算法及其在不同CT重建算法下的效能评估
深度学习胸部CT辅助诊断系统在急诊创伤人群中的应用
低钠血症在老年髋部骨折中的临床意义
腹腔镜子宫切除术老年患者术中局部脑氧饱和度变化的影响因素分析
帕金森病患者的角膜神经纤维变化特点分析
Orai1蛋白介导钙池操纵的钙内流在脓毒症小鼠CD4+T细胞免疫功能损害中的作用
继发于布氏杆菌脑膜脑炎的Miller-Fisher综合征一例
恶性潜能未定的胃血管球瘤一例
带状疱疹治疗及预防
肌萎缩侧索硬化症状前期研究的现状与启示
异位妊娠期待治疗的进展
影响CT图像上深度学习胰腺分割算法成功率的技术和临床因素
Pancreatic segmentation is widely used in surgical planning, diabetes assessment and detection and analysis of pancreatic tumors. Because the shape, size and density of the pancreas vary greatly, and it is closely related to adjacent organs such as intestine and blood vessels, it is still a difficult problem to accurately segment the pancreas on CT images. In order to understand the factors affecting the deep learning pancreatic segmentation algorithm, this study evaluated the segmentation effect of 82 venous abdominal CT images from public datasets, analyzed from three aspects: population and clinical data, CT technical factors and CT imaging performance, and used Dice similarity coefficient (DSC) to evaluate the segmentation accuracy. The results showed that the mean DSC of pancreatic segmentation was 78% ± 8%. Factors significantly associated with pancreatic segmentation effects included patient body mass index (r=0.34,P<0.01), abdominal visceral fat (r=0.51,P<0.000 1), pancreatic volume (r=0.41,P=0.001), standard deviation of intrapancreatic CT values (r=0.30,P=0.01), the median and mean of CT values at the critical 5 mm of the pancreas (r=-0.53,P<0.000 1;r=-0.52,P<0.000 1), CT Image pixel values (r=0.31,P=0.004)。 There was no significant correlation between DSC and height, sex and mean CT values of pancreas.
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