Pathology application examples
Ploidics: DNA Quantization and Ploidy Analysis
Segmentation results for one image of an image sequence, with Image Gallery shown
Typical ploidy histogram
Ploidy analysis is a test performed on cells using either an optical microscope hooked to a computer or a flow cytometer. It is based on the fact that tumors with a diploid pattern of DNA amounts tend to be benign. On the other hand, tumors with highly variable DNA patterns tend be more malignant.
The Ploidics™ product is a stand-alone, software product that performs image processing and image analysis on digital images captured with an optical microscope and stored on a computer. It also processes virtual slide images of any size and depth. Ploidics is an automatic tool to measure abnormal DNA content in individual cells in a mixed population of cells. It includes a fully automatic, powerful, and innovative segmentation process, an editing tool to manually annotate the segmentation result if desired, and an automatic computation of a histogram showing the DNA mass distribution of the cell population.
The Ploidics software product has the following capabilities:
- Automatic splitting of touching nuclei
- User selection of nuclei size used in the analysis
- User decision to keep or discard heterogeneous nuclei
- User specification of color band to be processed based on the stain selected
- User modification of segmentation results to select/deselect cells used in the Ploidy analysis
Ploidics is a stand-alone, software product that can be installed on any personal computer running Windows® XP or higher. Images need to be accessible from a hard drive or from network storage, and organized in folders to be processed by the software. All Ploidics measurements are calibrated and output in real-world units. The calibration value is manually entered in the Ploidics Software as a single value.
'ImagePath' DNA quantization and ploidy analysis
DNA Ploidy analysis sample
DNA quantization: ImagePath Application for Ploidy Analysis
The ImagePath Systems provides vertically integrated imaging products and reagents for the anatomic pathology laboratory.
Current offerings include ImagePath's remarkable integration of reagents, microscope, camera, high-speed computer and propriety imaging algorithms to permit ploidy analysis (DNA measurements in individual, Feulgen stained, cell nuclei) of a thousand or more nuclei in a few minutes, speeds never before thought possible.
This system also is optimized for full color image capture at resolutions of 1315 x 1024 pixels - which provides detail approaching the lens resolution of the microscope. These full color images can be annotated and saved, as well as exported to other graphics programs.
The ImagePath 300 consists of a vertically integrated system for performing rapid image-based DNA Ploidy Analysis with simple, intuitive, menu driven instructions, standardizing these procedures for improved accuracy and repeatability. Full user interaction with measurement results is provided via linked displays. Mouse actions in any image, graph or table causes corresponding data to be highlighted in the other windows.
'DRACCAR' DNA quantization and ploidy analysis
DNA Quantization and Ploidy application for anticancer research
ADCIS and the Francois Baclesse Cancer Centre in Caen jointly developed a turn-key system to perform DNA quantization and Ploidy analysis. These two tests are performed routinely by laboratories involved in cytopathology in oncology. Starting from image processing algorithms on an existing UNIX system 1, ADCIS ported all the image processing algorithms into the Aphelion™ environment on a PC running Windows. Porting this application to the PC with Aphelion improved the system performance and ease-of-use, while dramatically reducing the cost of the system by using the standard PC environment.
The system includes an optical microscope equipped with an automatic stage able to move in the X, Y and Z directions, a black and white camera mounted on the microscope, and a PC running Windows with a standard frame grabber board.
The image processing techniques involved in the system are rather complex, and can be described in the following steps:
- Acquisition of a series of images from the sample placed on the automatic stage. The motion of the stage is fully controlled by the application, with a set of predefined tasks that can be called from a user friendly environment based. Wizards are used to make it easy for the operator to re-configure the system.
- Live images are calibrated using a technique based on Optical Density Analysis, to insure that the densitometry of the cells is in the same range.
- Image segmentation to generate a set of small images each containing one cell, and a spreadsheet holding cell characteristics.
- A set of 19 parameters based on the shape, intensity, and texture are computed, and the measurements are saved in a spreadsheet. In this spreadsheet, each cell is now represented as an object in an Aphelion ObjectSet table.
- A manual classification is performed only once for any cancer location under study to sort cells into various categories according to their nuclear morphology: normal epithelial cells, lymphocytes, stromal cells, and abnormal epithelial cells with non standard size and texture.
- A model is derived from the training set, using techniques based on principal components analysis.
- In routine use, automatic classification is performed using the same statistical techniques.
- After analyzing the whole sample, the various ploidy histograms of well identified normal or abnormal cells are displayed in the Graphical User Interface.
During the development of the software, all results were matched against the results obtained with a flow cytometry system, pointing out the main benefits of image analysis: identification and elimination of debris and unwanted stromal cells, analysis of fixed formalin and paraffin embedded samples.
The following screen captures present two of the windows in the analysis.
The custom engineering work performed by ADCIS engineers included the definition of the GUI, the implementation of the image segmentation algorithms, the full control of the stage of the microscope, and the development of the classification module, which is now available as an ActiveX component. The stand-alone application was developed using Visual Basic, calling for the Aphelion ActiveX components and Toolkits.
Future development on the product will include more advanced statistical analysis, such as dispersion and dynamic clustering.
This application demonstrates the power of the Aphelion's ActiveX components in use. The system was developed quickly and is easy to maintain and expand as needed.
ADCIS and the GRECAN (bioticla) at Francois Baclesse Cancer Centre are also currently working together on related biology projects such as the immuno-marker analysis, and other original tools dedicated to experimental and clinical pathology.
Stereology Analyzer: Stereological analysis of 3D structures using 2D section or projection images
Stereology Analyzer is a simple to use software tool for reliably estimating quantifications of important 3D structures. This tool is general in its implementation, but has applicability to various scientific domains, most commonly in medicine, materials science, and geology.
The common factors in these fields are the need to characterize and quantify microscopic structures of interest ("SOI") and the use of very large images (i.e., virtual slides or composite images).
Where automated processes don't exist or fail to compute SOI parameters reliably, stereology is the method of choice to estimate these parameters. In fact, stereology is also frequently used to validate the proper performance of complex, automated algorithms. Stereology Analyzer is a faithful implementation of long-accepted stereological and statistical methods in the context of today's software technology.
|Science||Structures Of Interest|
|Medicine||Tumor, vessels, cell, hotspot, etc.|
|Materials||Grain, inclusion, boundary, pore, etc.|
|Geology||Pore, phase, etc.|
Image courtesy of Paulette Herlin
The term stereology was first introduced in 1961 when the International Society for Stereology (ISS) was founded by a small group of scientists, although the basis of stereology theory was defined more than 300 years ago. By definition, stereology is the science that studies the geometric relationship between a structure that exists in 3D space and a set of images of the same structure that are fundamentally defined in 2D space (images of slices, sections, or projections). Note that standard 2D image processing techniques will hardly provide 3D information from sections, except for the volume fraction value.
Stereology Analyzer Presentation
Stereology Analyzer implements long-standing and accepted stereology techniques that employ an interactive grid overlaid on regions of interest ("ROI") in a 2D image. Stereology Analyzer enables the user to optionally define one or more ROIs and a grid that overlays the ROIs or the whole image. The type and spacing of the grid can be adjusted by the user to achieve the best estimates of the SOI parameters contained within the ROIs. The types of grids are characterized by the geometric element that displays at the grid's nodes. Grid element alternatives include points, lines, frames, squares, and circles.
After the grid and ROIs are defined, the user manually highlights SOIs that are intersected by the grid elements. The number of grid elements contained within the ROIs and the number of highlighted SOIs are then automatically counted and used to compute SOI parameters. The automatic computations are based on classical stereological and statistical analyses. These computational results are then displayed on the computer's screen and can be exported into third party environments (e.g., Excel, Word) for display and further analysis specific to the applicable field.
Image Processing Algorithm Validation
Experts in the applicable field can use Stereology Analyzer to quickly compute unbiased estimates of SOI parameters on virtual slides. If used properly, Stereology Analyzer is an effective alternative for image processing developers to validate a sequence of complex image processing algorithms. In addition, while complex algorithms require careful validation of their results, stereology results require no validation since results are derived from a standard statistical analysis of grid elements and user-highlighted SOIs.
In the field of medicine, when the SOIs cannot be highlighted by a specific staining (e.g., histochemical or immunohistochemical stainings), or when staining is not optimal or tissue is heterogeneous, then the techniques of stereology are the best alternative for estimating the parameter values of SOIs.
When specific staining is effective, different image processing and analysis algorithms can be developed that vary in their complexity, accuracy, and efficiency. Stereology Analyzer is a powerful tool for establishing the quantification accuracy that an algorithm sequence should achieve to be relied on. The statistical sampling process used in stereology science diminishes the difficult problem resulting from tissue heterogeneity. The special strength of stereology is that it always provides unbiased estimates of SOI parameters for any complexity of sample tissue. When combining stereology and image processing in pathology and scanning microscopy fields, the user has a broad set of powerful tools to characterize SOIs on virtual slides.
The Stereology Analyzer has the following capabilities:
- Grid: Uniform and user definable in density and type. Types include points, frames, squares, circles, and lines
- Region of interest: User definable, multiple regions allowed, can be any shape (e.g., ellipse, rectangle, and free-hand drawing)
- Undesired tissue: User can exclude undesired tissue and other undesired area regions
- Input: Input image formats are TIFF, tiled TIFF, and JPEG
- Output: Volume fraction, numeric density of SOI profiles per unit area
- Keyboard shortcuts: Provided to speed up the SOI marking process
-  G. Matheron, 1975. Random set and integral geometry. J. Wiley and sons, New York, USA.
-  E.R. Weibel, 1981. Stereological methods in cell biology: where are we--where are we going? J. Histochem. Cytochem., 29, 1043-1052
-  H. J. G. Gundersen, R. Østerby, 1981. Optimizing sampling efficiency of stereological studies in biology: or “Do more less well!” J. Microsc., 121, 65-73.
-  V. Howard and M. Reed, 1998. Unbiased Stereology. Three-dimensional measurement in microscopy. Microscopy handbooks 41, Bios Scientific Publishers, UK.
-  J. Russ, R. Dehoff, 1999. Practical Stereology, 2nd Edition, Plenum Press, New York.
-  L. Kubınova, X. W. Mao, J. Janacek, J. O. Archambeau, 2003. Stereology Techniques in Radiation Biology, Radiation Research 160, 110-119.
The concept of this module is derived from the Stereology expertise of Paulette Herlin and her original development works at the Centre François Baclesse Cancer Center. The expertise of Dr Dragos Vasilescu, PhD, in the field of stereology and the use of combined grids, University of British Columbia, Vancouver, V6Z 1Y6, Canada, helped ADCIS to develop the latest version of the Stereology Analyzer Software product.
Main benefits of Image Quality Extension:
- Perform measurements when image processing is not possible or too complex
- Best method to validate image processing algorithms on very large images
- Provide unbiased measurements and an accurate volume estimate
- Estimate 3D characteristics of structures from a 2D slice or projections
- Compatible with most virtual slide acquisition devices