Speaker
Description
Multi-view and multi-tile imaging offer great potential for enhancing resolution, field of view, and penetration depth in microscopy. Here we present multiview-stitcher, a versatile and modular python package for 2/3D image reconstruction that leverages registration and fusion algorithms readily available within the ecosystem for efficient use within an extensible and standardized framework. A key feature consists of scalability to large datasets, which is achieved by means of chunk-aware processing with support for full affine spatial transformations. Multiview-stitcher provides interoperable user interfaces in the form of a modular python API for use as a library and a napari plugin for interactive stitching of image layers. Importantly, it allows users to quickly adopt and compare the results of applying different reconstruction modalities within custom workflows. To demonstrate the usability and versatility of multiview-stitcher we showcase its use for reconstructing multi-view and multi-tile (light sheet) acquisitions in different configurations, 3D high content screening datasets and cryo-EM montages.
| Authors | Marvin Albert*, Arthur Michaut, Artemiy Golden, Alexander Wilhelmi, Jean-Yves Tinevez |
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| Keywords | Image reconstruction, registration, fusion, stitching, large data, research software, interoperability, python |
