Fijiyama: a registration tool for 3D multimodal time-lapse imaging

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.

Automated summary

The increasing interest in biomedical 3D imaging devices from animal and plant research communities has led to new research topics. Combining outputs from imaging devices such as X-ray CT and MRI scans allows non-destructive observation of tissue anatomy, structure, and function in time-lapse multimodal imaging experiments. Living samples cannot remain in these devices for long periods, leading to variations in shape, position, and orientation in the acquired images. A preprocessing step of 3D registration is required to address these variations before analysis. Fijiyama, a Fiji plugin built upon biomedical registration algorithms, aims to facilitate automatic alignment of 3D images for non-specialists.