Investigation of the Hashing Algorithm Extension of Depth Image Matching for Liver Surgery
Investigation of the Hashing Algorithm Extension of Depth Image Matching for Liver Surgery
复制标题
肝脏手术深度图像匹配的哈希算法扩展研究
DOI：
10.1007/978-3-030-78465-2_44
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Hiroshi Noborio
中科院分区：
文献类型：
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作者：
Satoshi Numata;Masanao Koeda;Katsuhiko Onishi;Kaoru Watanabe;Hiroshi Noborio
我们已经开发了一些肝脏姿态估计方法，以实现肝脏手术导航系统，可以支持需要非常精确地了解肝脏血管信息的外科医生。这些方法使用从患者身上扫描的3D肝脏模型和深度相机扫描的2D图像来尽可能准确地估计肝脏的姿势。由于去年开发了一种新的基于简单高速图像哈希算法的姿态估计方法，我们正在努力提高该方法的准确性和适用性，用于实时肝脏姿态跟踪。在本文中，我们研究了如何将深度学习方法用于肝脏姿态估计及其对深度相机扫描的2D图像的跟踪。我们研究了多层感知器神经网络如何学习和估计以四元数形式表示的肝脏旋转。实时手术导航系统需要结合包括深度学习方法在内的多种估计方法有效实现。
We have developed some liver posture estimation methods for achieving a liver surgical navigation system that can support surgeons who needs to know very precise information about vessels in the liver. Those methods use 3D liver models scanned from patients and 2D images scanned by depth cameras for estimating the liver posture as accurately as possible. Since a new posture estimation method using simple and high-speed image hashing algorithm was developed last year, we are trying to improve the method in accuracy and applicability for the real-time liver posture tracking. In this paper, we examine how deep learning methods can be used for liver posture estimation and its tracking over the 2D images scanned from depth cameras. We study how can a multi-layer perceptron neural network learn and estimate the liver rotation expressed in quaternion form. The real-time surgical navigation system should be efficiently implemented by combining multiple estimation methods including the deep learning method.
《肝脏手术 3D 模型跟踪的性能和准确性分析》
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
Satoshi Numata;Masanao Koeda;Katsuhiko Onishi;Kaoru Watanabe and Hiroshi Noborio
通讯作者：
Kaoru Watanabe and Hiroshi Noborio