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.

中文翻译：

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斐济山：3D多模态延时成像的注册工具

动植物研究界对生物医学3D成像设备的兴趣日益浓厚，导致出现了新的话题。通过结合诸如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无损观察组织的解剖结构，结构和功能。但是，活样本不能长时间保留在这些设备中。活体样本的手动定位和自然生长会在获取的图像中引起形状，位置和方向的变化，这需要在分析之前进行3D配准的预处理步骤。当结合来自突出各种组织结构的设备的观察结果时，此配准步骤变得更加复杂。识别模态的图像不变性具有挑战性，并且可能导致棘手的问题。斐济山（Fijiyama）是建立在生物医学注册算法基础上的斐济插件，其针对非专业人员，以促进在连续时间和/或不同成像系统下自动对齐3D图像。在四个案例研究中评估了它的多功能性，这些案例结合了多模式和时间序列数据，涵盖了从微观到宏观的范围。