Correspondence points can be detected automatically using fiducial markers ( Preibisch et al., 2010), and paired using local descriptors. But incorrect point positioning leads to alignment mismatches, and the 3D segmentation task is sometimes time-consuming. Registration of 3D images can be performed by identifying correspondences between points ( Peng et al., 2011) or segmentations ( Grocott et al., 2016) defined by the user. Its versatility was assessed on four case studies combining multimodal and time series data, spanning from micro to macro scales. 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. Identifying image invariants over modalities is challenging and can result in intractable problems. This registration step becomes more complex when combining observations from devices that highlight various tissue structures. 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. However, living samples cannot remain in these devices for a long period.
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. The increasing interest of animal and plant research communities for biomedical 3D imaging devices results in the emergence of new topics.
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