Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning.

Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning.
复制标题

使用深度 CNN 和强化学习的微分形肺配准。

DOI：
10.1007/978-3-030-00946-5_28
复制
发表时间：
2018
期刊：
Image analysis for moving organ, breast, and thoracic images : third International Workshop, RAMBO 2018, fourth International Workshop, BIA 2018, and first International Workshop, TIA 2018, held in conjunction with MICCAI 2018, Granada,...
影响因子：
--
通讯作者：
JoséEstépar,RaúlSan
JoséEstépar,RaúlSan
中科院分区：
--
文献类型：
--
作者：
Onieva,JorgeOnieva;Marti-Fuster,Berta;delaPuente,MaríaPedrero;JoséEstépar,RaúlSan

文献摘要

相似文献

图像配准是医学成像领域中的一个众所周知的问题。在本文中，我们专注于注册胸部吸气和呼气计算机断层扫描（CT）扫描从同一个病人。我们的方法恢复的双纯弹性位移矢量场（DVF）的联合回归的正变换和逆变换。我们的架构基于RegNet网络，但我们实施了一种强化学习策略，可以适应大型训练数据集。我们的研究结果表明，我们的方法比RegNet方法具有更低的估计误差。
Image registration is a well-known problem in the field of medical imaging. In this paper, we focus on the registration of chest inspiratory and expiratory computed tomography (CT) scans from the same patient. Our method recovers the diffeomorphic elastic displacement vector field (DVF) by jointly regressing the direct and the inverse transformation. Our architecture is based on the RegNet network but we implement a reinforced learning strategy that can accommodate a large training dataset. Our results show that our method performs with a lower estimation error for the same number of epochs than the RegNet approach.