ImageJ Macros


This workflow describes a deep-learning based pipeline for reliable single-organoid segmentation and tracking in 2D+t high-resolution brightfield microscopy of mouse mammary epithelial organoids. The pipeline involves a four-layer U-Net to infer semantic segmentation predictions, adaptive morphological filtering to establish candidate organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking step to associate the corresponding organoid instances in time.

It is particularly focused on automatically detecting an organoid located approximately in the center of the first frame and track all its subsequent instances in the remaining frames, emphasizing on accurate organoid boundary delineation. Furthermore, segmentation network was trained using plausible pix2pixHD-generated bioimage data. Syntheric image simulator code and data are also available here.

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It stitches 3D tiles from terabyte-size microscopy datasets. Stitching does not require any prior information on the actual positions of the tiles, sample fiducials, or conversion of raw TIFF images, and the stitched images can be explored instantly.

MosaicExplorerJ was specifically designed to process lightsheet microscopy datasets from optically cleared samples. It can handle multiple fluorescence channels, dual-side lightsheet illumination and dual-side camera detection.

GPU-Accelerating ImageJ Macro Image Processing Workflows Using CLIJ

Submitted by czhang on Thu, 04/27/2023 - 09:55

This chapter is part of this book. The chapter introduces GPU-accelerated image processing in ImageJ/Fiji. The reader is expected to have some pre-existing knowledge of ImageJ Macro programming. Core concepts such as variables, for-loops, and functions are essential. The chapter provides basic guidelines for improved performance in typical image processing workflows.


The authors present an ImageJ-based, semi-automated phagocytosis workflow to rapidly quantitate three distinct stages during the early engulfment of opsonized beads.