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

Area of Science:

• Biomedical imaging
• Computational biology
• Image analysis

Background:

• Biomedical 3D imaging, including X-ray CT and MRI, is crucial for observing tissue anatomy, structure, and function.
• Acquired images often require 3D registration due to variations in living sample positioning, growth, and multimodal data acquisition.
• Aligning images from different modalities presenting diverse tissue structures poses significant challenges.

Purpose of the Study:

• To introduce Fijiyama, an open-source Fiji plugin designed for automatic 3D image registration.
• To facilitate the alignment of 3D images acquired over time and/or with different imaging systems for non-specialists.
• To address the complexities of multimodal image registration in biomedical research.

Main Methods:

• Fijiyama is a Java-based Fiji plugin utilizing established biomedical registration algorithms.
• The software is available through the official Fiji release, with comprehensive documentation provided.
• Versatility was evaluated using four case studies involving multimodal and time-series data across different scales.

Main Results:

• Fijiyama enables automatic alignment of 3D images from successive time points and/or different imaging systems.
• The plugin simplifies the preprocessing step required for time-lapse multimodal imaging experiments.
• Demonstrated versatility across micro to macro scales in case studies combining multimodal and time-series data.

Conclusions:

• Fijiyama provides a user-friendly solution for automatic 3D image registration in biomedical research.
• The plugin enhances the analysis of complex datasets from time-lapse and multimodal imaging.
• Facilitates non-destructive, in-depth study of biological samples for researchers.