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Performs the four pre-processing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is the same. PreMosa is able to detect and project the signals of interest, despite the presence of background noise. It uses a method that accurately aligns the planes even if their alignment is priorly unknown. The tool uses a particular order to pre-process individual tasks that allows it to reduce the required memory and computation time.


Applies a recursive normalization algorithm to stitch large-scale immunofluorescence-based mosaics without incurring vignetting seams. QuickStitch works successfully for tissues of differing size, morphology, and fluorescence intensity. It requires no specific information about the imaging system. It is provided as an open-source tool that is both user friendly and extensible, allowing straightforward incorporation into existing image processing pipelines. This enables studies that require accurate segmentation and analysis of high resolution datasets when parameters of interest include both cellular-level phenomena and larger tissue-level regions of interest.