Pruning strategies for efficient online globally consistent mosaicking in fetoscopy

Pruning strategies for efficient online globally consistent mosaicking in fetoscopy
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DOI:
10.1117/1.jmi.6.3.035001
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发表时间:
2019-07-01
影响因子:
2.4
通讯作者:
Ourselin, Sebastien
Ourselin, Sebastien
中科院分区:
其他
文献类型:
--
作者:
Tella-Amo, Marcel;Peter, Loic;Ourselin, Sebastien

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双胎输血综合征是同卵双胞胎在胎盘中共享某种血管连接模式的疾病。这会导致血流失衡,如果不及时治疗,可能会导致双胞胎死亡。为了治疗这种情况,外科医生用胎儿镜探查胎盘,以找到并光凝所有相互缠绕的血管连接。然而,胎儿镜视野的缩小使它们的定位和总体概览变得复杂。通过在探索时创建的在线马赛克可以实现更有效的探索。目前,诸如束平差之类的准确的、全局一致的算法由于其离线特性而无法使用,而在线算法则缺乏足够的准确性。我们引入了两种修剪策略,有助于以顺序方式使用捆绑调整:(1)一种有效利用电磁跟踪系统潜力的技术,以避免空间不一致的图像对之间不必要的匹配尝试,以及(2)图像的聚合表示,我们将其称为超帧,它可以降低全局一致方法的计算复杂性。合成数据集和基于模型的数据集的定量和定性结果表明,效率和准确性之间有更好的权衡。 (C) 作者。由 SPIE 根据 Creative Commons Attribution 4.0 Unported 许可证发布。
Twin-to-twin transfusion syndrome is a condition in which identical twins share a certain pattern of vascular connections in the placenta. This leads to an imbalance in the blood flow that, if not treated, may result in a fatal outcome for both twins. To treat this condition, a surgeon explores the placenta with a fetoscope to find and photocoagulate all intertwin vascular connections. However, the reduced field of view of the fetoscope complicates their localization and general overview. A much more effective exploration could be achieved with an online mosaic created at exploration time. Currently, accurate, globally consistent algorithms such as bundle adjustment cannot be used due to their offline nature, while online algorithms lack sufficient accuracy. We introduce two pruning strategies facilitating the use of bundle adjustment in a sequential fashion: (1) a technique that efficiently exploits the potential of using an electromagnetic tracking system to avoid unnecessary matching attempts between spatially inconsistent image pairs, and (2) an aggregated representation of images, which we refer to as superframes, that allows decreasing the computational complexity of a globally consistent approach. Quantitative and qualitative results on synthetic and phantom-based datasets demonstrate a better trade-off between efficiency and accuracy. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.