| 摘要: |
| 当前混合现实(MR)技术在数字医疗领域正日益受到广泛关注. 以脑胶质瘤医学影像分析混合现实技术为对象, 提出基于深度学习模型3D UNet的MR脑胶质瘤定位与区域分割算法, 采用基于面绘制方法对脑胶质瘤影像进行多结构组织的三维绘制与优化, 提出了基于交互式无标识和基于标识图的移动混合现实三维注册跟踪与视觉空间共享算法, 实现MR多设备的第三视角空间实时共享, 设计并实现了面向脑胶质瘤医学影像分析的混合现实原型系统. 实验结果表明所提方法能有效实现MR脑胶质瘤检测、分割与三维重建, 通过MR移动设备的实时共享, 实现脑胶质瘤医学影像混合现实分析, 有效支撑脑胶质瘤辅助诊断与治疗, 也为手术术前规划、医学教育培训等提供了新的方法. |
| 关键词: 混合现实 医学影像 脑胶质瘤 深度学习 面绘制 空间共享 |
| DOI：10.13328/j.cnki.jos.006393 |
| 分类号:TP391 |
| 基金项目:国家重点研发计划国际科技创新合作专项(2017YFE0123000) |
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| Mixed Reality Technology for Medical Imaging Analysis of Glioma |
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CAI Lin-Qin1,2, YI Wen-Yuan1, HUANG Yu-Ting1, DAI Yu-Han1
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1.School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2.Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education (Chongqing University of Posts and Telecommunications), Chongqing 400065, China
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| Abstract: |
| At present, mixed reality (MR) technology is gaining increasingly attention in digital medicine. Targeted at MR of glioma medical image analysis, this study proposes an MR glioma location and regional segmentation algorithm based on the 3D UNet deep learning model, and uses the surface rendering method to render and optimize multi-structure tissue of the glioma image in three-dimensional space. On this basis, three-dimensional registration tracking and visual space sharing algorithms are presented using the interactive markerless and the marker-based graphs for mobile MR to achieve the real-time third-view space sharing for MR multi-devices. In addition, an MR experimental system is designed and implemented for glioma medical image analysis. The experimental results show that the methods proposed in this paper can effectively realize the detection, segmentation and three-dimensional reconstruction of the brain glioma. Through the real-time sharing of mobile MR devices, the proposed methods can effectively achieve MR analysis of glioma medical images to support the auxiliary diagnosis and treatment of glioma, and it also can provide new methods for preoperative planning, medical education and training, etc. |
| Key words: mixed reality (MR) medical imaging glioma deep learning surface rendering space sharing |
