Institut Français de la Vigne et du vin, Pôle National Matériel Végétal, UMT Géno-Vigne®, 34060 Montpellier Cedex 1, France.
Bioinformatics. 2021 Jun 16;37(10):1482-1484. doi: 10.1093/bioinformatics/btaa846.
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.
Fijiyama is an open source software (GPL license) implemented in Java. The plugin is available through the official Fiji release. An extensive documentation is available at the official page: https://imagej.github.io/Fijiyama.
Supplementary data are available at Bioinformatics online.
动物和植物研究界对生物医学 3D 成像设备的日益关注导致了新主题的出现。通过结合 X 射线 CT 和 MRI 扫描等成像设备的输出，可以在时变多模态成像实验中对组织的解剖结构、结构和功能进行非破坏性观察。然而，活体样本不能在这些设备中长时间保留。活体样本的手动定位和自然生长会导致在获取的图像中形状、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。当结合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。在模态之间识别图像不变量具有挑战性，并且可能导致难以解决的问题。Fijiyama 是一个基于生物医学配准算法构建的 Fiji 插件，旨在为非专业人士提供便利，使其能够自动对齐在不同时间和/或使用不同成像系统获取的 3D 图像。它的多功能性在四个案例研究中进行了评估，这些案例研究结合了多模态和时间序列数据，涵盖了从微观到宏观的尺度。
Fijiyama 是一个开源软件（GPL 许可证），用 Java 实现。该插件可通过官方 Fiji 版本获得。官方页面提供了详细的文档：https://imagej.github.io/Fijiyama。
补充数据可在 Bioinformatics 在线获取。
