Details

Name
Identification Based on Color
IQbot ID
IQBOT-0009/td>
Version
1.0.0
Research Area(s)
All
Processing Time Expectation
Less than 2 Minutes per Image
Acquisition Hardware
Wide-Field, Optical Microscope in Bright-Field (Transmission) Mode Using a Color Mosaic CCD or Monochrome CCD with RGB Filter
Techniques, Dyes, Stains, & Proteins
Any (e.g., Oil Red O, Safranin-O, Masson's Trichrome, etc.)
Sample Context
In Tissue
Name
Identification Based on Color
IQbot ID
IQBOT-0009/td>
Version
1.0.0
Research Area(s)
All
Processing Time Expectation
Less than 2 Minutes per Image
Acquisition Hardware
Wide-Field, Optical Microscope in Bright-Field (Transmission) Mode Using a Color Mosaic CCD or Monochrome CCD with RGB Filter
Techniques, Dyes, Stains, & Proteins
Any (e.g., Oil Red O, Safranin-O, Masson's Trichrome, etc.)
Sample Context
In Tissue

Output Parameters

Total Tissue Area (pixels)
The total area of tissue within the image.
Positive Area (pixels)
The total count of pixels within "positive" regions (color of interest indicating "positive" is identified by the prefix in the file name).
Total Tissue Area (pixels)
The total area of tissue within the image.
Positive Area (pixels)
The total count of pixels within "positive" regions (color of interest indicating "positive" is identified by the prefix in the file name).

Image Acquisition


This IQbot was developed to analyze images acquired using a wide-field, optical microscope in bright-field (transmission) mode using a color mosaic CCD or monochrome CCD with RGB filter. For accurate analysis of tissue area, section must be counterstained. The image name must also be prefixed with the ID color of interest (e.g., "blue_*.tif" for blue, "red_*.tif" for red, etc.)

 
 

The Identification Based on Color IQbot has been built to accommodate the following imaging types only.

 24-bit RGB
 .tif
  .tiff
  .bmp
 .jpg
.jpeg
 
 

As we reference for IQbots in general here, the Identification Based on Color IQbot is only ultimately effective if you upload high quality images for processing. Please ensure that any images you upload follow the below guidelines specific to this IQbot.

High-Resolution:  The quality of the output data you receive is largely dependent on the input image resolution. Please provide high-resolution images to ensure that the IQbot can most effectively differentiate between features.
In-Focus:  Please produce the most in-focus images possible for analysis. Images with high background contrast will result in more accurate and useful data and overlays.
Properly Saturated:  If your submitted images are significantly undersaturated or oversaturated, it is possible that the IQbot will be unable to detect structures, or may group structures together. Please ensure that the images you upload are saturated appropriately.
Clear of Artifacts:  Please take care to avoid creating bubbles, debris, or otherwise inducing artifacts into your image. Such additional features may corrupt the resulting data.
Clear of Accessary Information:  Please do not submit images retaining any annotations, legends, timestamps, or other markings. Every part of the images that you upload will be analyzed and included in the output data.
 
 
 

If you are uncertain whether or not your images meet the quality standards for which this IQbot was validated, or have additional questions relating to image quality, please view our FAQs or contact the ImageQuantify.com support team.

 

     (216) 678-9258           help@imagequantify.com

 

Validation Information


This IQbot has been successfully validated for use, and a copy of its validation certificate accompanies the results of each order it analyzes. In accordance with general ImageQuantify.com validation procedures, this IQbot has been found to provide quantitative information equivalent to that of a manual observer, but with no variability and increased breadth of information.

 
 

The first step of the validation process was to establish the ground truth against which the IQbot’s efficacy would be compared. For the purpose of the Identification Based on Color IQbot, ground truth was defined on a manual observer basis. Trained observers were guided through a standardized, semi-automated process to produce output data from a specially selected validation image set. This image set represented the entire range of expected output values (e.g., different colors, stains, contexts, and densities). Images were presented for analysis in a randomized order and deidentified to protect against observer biases. The observer mean for each parameter measured was accepted as ground truth.

By analyzing the inter- and intraobserver variability present in the manual observation process, acceptable criteria were established so as to compare the IQbot’s output to that of a trained replacement observer. Ultimately, the IQbot’s performance proved to offer significant upgrades over manual or semi-automated analysis techniques, and validation was considered successful. Further considerations ensured that the IQbot rejected and flagged concerning image-specific outputs and performed within operation time constraints.

 

The Identification Based on Color IQbot segments and quantifies regions of a specific "color" in tissue histological sections. This color of interest for analysis is provided to the IQbot via the filename prefix for a given image (i.e., "blue_*.tif" for blue, "red_*.tif" for red). As a result, this IQbot is agnostic to the structure and location of a target color or stain. For example, it could be used to quantify fibrous tissue or collagen in Masson's trichrome sections, lipid deposits in Oil Red O stained sections, cartilage in Safranin-O stained sections, etc. Output parameters of the IQbot include total area of color or stain of interest (Positive Area) and, with the aid of a counterstain, the area of tissue in the image (Total Tissue Area) is analyzed to enable normalization of the "positive" staining content. Lastly, in addition to the quantitative metrics, a pseudo-colored image is generated for each image analyzed by the IQbot in which the segmented color of interest is pseudo-colored green.

DEMO IMAGE
IQBOT APPLIED IMAGE

 

SAMPLE DATA
Image Total Tissue Area
(pixels)
Positive Area
(pixels)
Sample 2181745 1267992