Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh


A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin Squassh is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

3D object based colocalization using KNIME


These two similar KNIME workflow solutions take 3D data stacks to segment the spots first, using local thresholding with subsequent morphological operations in order to remove noise. Colocalization is then defined by overlapping or center point distance between segmented objects. Further filtering such as overlapping ratio or distance range is done through KNIME table processing.

Two different types are available. 

  1. colocalization based on overlapping
  2. colocalization based on distance between object centers

Sample images: Smapp_Ori files

Chapter 4 in the documentation. 



‘’’Squassh’’’ is a tool for 2D and 3D segmentation and quantification of subcellular shapes in fluorescence microscopy images. It provides globally optimal detection and segmentation of objects with constant internal intensity distribution, followed by object-based colocalization analysis. The segmentation computed by Region Competition can optionally correct for the PSF of the microscope, hence providing optimally deconvolved segmentations. Part of the mosaic suite

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