Nuclei segmentation and shape measurement

Prerequisites

Before starting this lesson, you should be familiar with:

• Thresholding

• Connected component labeling

• Object shape measurements

Learning Objectives

After completing this lesson, learners should be able to:

• Create a basic image analysis workflow.

• Understand that bioimage analysis workflows consist of a sequence of image analysis components.

• Segment nuclei in a 2D image and measure their shapes and understand the components (concepts and methods) that are needed to accomplish this task.

• Draw a biophysically meaningful conclusion from applying an image analysis workflow to a set of images.

Motivation

Detecting a set of objects in an image, counting them and measuring certain characteristics about their morphology is probably the most frequently occurring task in bioimage analysis. Depending on the image, even this task could become quite challenging and the workflow could become quite complex. Here we start with a relatively simple image where combining a minimal set of image analysis components into a simple workflow does the job.

Concept map

Figure

Activities

Segment 2D nuclei

• Input images
  • xy_8bit__mitocheck_incenp_t1.tif
  • xy_8bit__mitocheck_incenp_t70.tif
  • The images are two time points of a time lapse experiment where the INCENP gene was subjected to siRNA knock-down. The data are taken from the published mitocheck screen. In this screen the authors carried out a genome-wide phenotypic profiling of each of the ~21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed them to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival.
  • The analysis that we do here is, of course, simpler than what the authors did in the publication, but the essence is already very similar. In addition, to simplify the task we work here on images that were cropped and slightly denoised.
• Workflow
  • Apply the workflow outlined above (see concept map) to both images.
  • The modules listed in “Prerequisites” contain the information as to how to conduct each step of the workflow.
  • The nuclei in both images look quite different. Find shape measurements that quantify this.

ImageJ GUI

• [ Process › Binary › Options… ]
  • Black background
• Open one of the input image (see above)
• [ Image › Duplicate… ]
  • Title = binary
• Draw a line profile to find a good threshold
  • Use the straight line tool in the Fiji menu bar
  • [ Analyze › Plot Profile ]
    • [ Live ] and move the line around, including nuclei and background pixels
• [ Image › Adjust › Manual Threshold… ]
  • Min = 25
  • Max = 255
• [ Process › Binary › Convert to Mask ]
• [ Plugins › MorphoLibJ › Binary Images › Connected Components Labeling ]
  • connectivity = 4
  • type= 8 bits
• [ Image › Lookup Tables › glasbey_on_dark ]
• [ Plugins › MorphoLibJ › Analyze › Analyze Regions ]
  • area

ImageJ Macro

/**
 * 2D Nuclei segmentation (simple workflow)
 * 
 * Requirements:
 *   - Update site: IJPB-Plugins (MorpholibJ)
 * 
 */

// Threshold parameter
threshold = 25;

// Code
run("Close All");
run("Options...", "iterations=1 count=1 black do=Nothing");

analyseNuclei( "INCENP_T1", "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t1.tif", threshold );
analyseNuclei( "INCENP_T70", "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t70.tif", threshold );

run("Tile");

function analyseNuclei( name, filePath, threshold )
{
	open(filePath);
	rename(name);
	setMinAndMax(0, 100);
	run("Duplicate...", "title=" + name + "_binary" );
	setThreshold(threshold, 65535);
	run("Convert to Mask");
	run("Connected Components Labeling", "connectivity=4 type=[8 bits]");
	run("glasbey_on_dark");
	run("Analyze Regions", "area");
}

skimage and napari

# %%
# Import modules
import napari
from OpenIJTIFF import open_ij_tiff
from skimage.measure import label, regionprops_table
import pandas as pd

# %%
# Instantiate the napari viewer
napari_viewer = napari.Viewer()

# %%
# Read and inspect the image:
fpath = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__mitocheck_incenp_t1.tif'
image_t1, axes_t1, voxel_size_input_t1, units_t1 = open_ij_tiff(fpath)

napari_viewer.add_image(image_t1, name='frame 1')


# %%
def nuclei_quantification(image, viewer, thr):
    print(f"Threshold: {thr}")
    # Binarize the image:
    image = image > thr
    viewer.add_labels(image, name='binary')
    # Find labels:
    img_labels = label(image)
    viewer.add_labels(img_labels, name='labels')
    # Obtain cell properties:
    properties = regionprops_table(
        img_labels,
        properties = {'label', 'area'}
    )
    # Print areas for each cell:
    areas = pd.DataFrame(properties)
    print(areas)

# %%
# Run workflow on each image:
threshold = 25
nuclei_quantification(image_t1, napari_viewer, threshold)

# Now try running the workflow using the second image!

Assessment

Explanations

Follow-up material

Recommended follow-up modules:

Learn more: