Fijiyama: a registration tool for 3D multimodal time-lapse imaging
Fijiyama: a registration tool for 3D multimodal time-lapse imaging
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DOI：
10.1093/bioinformatics/btaa846
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发表时间：
2021-05-15
期刊：
影响因子：
5.8
通讯作者：
Moisy, Cedric
中科院分区：
文献类型：
--
作者：
Fernandez, Romain;Moisy, Cedric
动物和植物研究社区对生物医学3D成像设备的兴趣日益增加，导致新课题的出现。通过结合X射线CT和MRI扫描等成像设备的输出，可以在延时多模态成像实验中非破坏性地观察组织的解剖结构、结构和功能。然而，活体样本不能在这些装置中长时间保留。活体样本的手动定位和自然生长会导致所采集图像的形状、位置和方向发生变化，这需要在分析之前进行3D配准的预处理步骤。当组合来自突出显示各种组织结构的设备的观察结果时，该配准步骤变得更加复杂。识别模态上的图像不变量具有挑战性，并且可能导致棘手的问题。Fijiyama是一个基于生物医学配准算法的斐济插件，旨在帮助非专业人员自动对齐连续时间和/或不同成像系统采集的3D图像。它的多功能性进行了评估的四个案例研究相结合的多模态和时间序列数据，从微观到宏观尺度。
The increasing interest of animal and plant research communities for biomedical 3D imaging devices results in the emergence of new topics. The anatomy, structure and function of tissues can be observed non-destructively in time-lapse multimodal imaging experiments by combining the outputs of imaging devices such as X-ray CT and MRI scans. However, living samples cannot remain in these devices for a long period. Manual positioning and natural growth of the living samples induce variations in the shape, position and orientation in the acquired images that require a preprocessing step of 3D registration prior to analyses. This registration step becomes more complex when combining observations from devices that highlight various tissue structures. Identifying image invariants over modalities is challenging and can result in intractable problems. Fijiyama, a Fiji plugin built upon biomedical registration algorithms, is aimed at non-specialists to facilitate automatic alignment of 3D images acquired either at successive times and/or with different imaging systems. Its versatility was assessed on four case studies combining multimodal and time series data, spanning from micro to macro scales.