Resolve Intraoperative Brain Shift as Imitation Game
Resolve Intraoperative Brain Shift as Imitation Game
复制标题
将术中大脑转移视为模仿游戏
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Maier
中科院分区：
文献类型：
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作者：
X. Zhong;Siming Bayer;N. Ravikumar;Norbert Strobel;A. Birkhold;M. Kowarschik;R. Fahrig;A. Maier
由开颅术和组织操作（脑移位）引起的软组织变形限制了在图像引导神经外科手术中使用术前图像覆盖，因此降低了手术的准确性。实时补偿大脑移动的廉价方式是超声（US）。在这种情况下，研究的核心课题是术前MR和术中US图像的非刚性配准。在这项工作中，我们提出了一种基于学习的方法来应对这一挑战。解决术中脑移位被认为是一种模仿游戏，其中使用多任务网络训练MR上每个标志的最佳动作（位移）。结果表明，平均目标误差为1.21 ± 0.55 mm。
Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.