An image selection model is a series of filters which exclude undesirable images within a sample from use in calibration curve generation or dose estimation. Excluded images are still present in the sample and can be viewed in the metaphase image viewer within which they can be manually re-included.
Image selection models can be applied to a sample in three ways:
Note when an image selection model is applied within either wizard, all images in each selected sample will first be included (meaning there will be no excluded images from previously applied models) before the chosen model is applied.
Image selection models always only exclude images. They do not re-include images excluded by a previously applied image selection model or by manual exclusion except when automatically applied by a wizard (as described above). If this is not the desired behaviour and you would like to apply the image selection model to a “clean” sample with no images excluded, click the “Include All” button before loading the new image selection model.
The dialog found to the right can be found in the metaphase image viewer. It is used here to show what properties can be modified in an image selection model. When viewing a list of image selection models within wizards and elsewhere, model property values are shown after the model name using an abbreviation for each property. These abbreviations are listed below, within the header for each property. Properties of the model are as follows:
1. Load a set of processed calibration and test samples.
2. Create a sample report with all samples
3. To create new Image Exclusion Filter thresholds: Examine distributions to find thresholds (as +S.D. above or below the mean value for the test) that can be applied to the majority of samples that remove a subset of images. This should be based on outlier values that distinguish the majority of images for a test from rarer images. For example, we expect 46 chromosomes in a metaphase cell. Most images will contain <60 objects (chromosomes + nuclei). Images with >60 objects are infrequent and are more likely to indicate excessive sister chromatid separation or multiple metaphase cells, or a lot of debris may be present.
4. To test a threshold for a test, open the metaphase image viewer for each sample, click on “apply image filters”, and check the box corresponding to the threshold you wish to apply, then change the default threshold value(s) to those you determined using the Sample Report. The number of images in the subset will usually be reduced from the complete set of images. Select view excluded images and review these. Verify that you want to remove these images by sorting the list by, for example, object count (top right selection), and carefully reviewing images close to the threshold boundary that you have chosen. You can scroll through the list using the green arrows (bottom left), or jump to particular images (threshold is shown in brackets) using the file name selector. If necessary, adjust the threshold and repeat the filtering and review step until you have selected a threshold. Record this threshold and the associated test.
5. Repeat step 4 for the other tests that you wish to apply to the data. The filtering is cumulative: images that are removed by one test, if selected for removal by another, will not be repeatedly eliminated.
6. After all of the image selection filters have been determined, then decide whether additional thresholds are required based on sorting. The combined Z score or group bin methods can sort images from highest to lowest quality, then the top X images can be selected for dose estimation or calibration curve generation. X should be at least 250, however if a larger number of high quality images pass the preceding tests (steps 4 and 5), then the top scoring image count can be higher (≥ 500).
7. Save the combined image selection model on the “apply image selection model” window by selecting “Save Current User Customized Model” and assigning a name to the model. Hint: assign a model name that describes the filters used in the model for future reference. The model name will appear in you list of models that can be applied to future samples.
8. Note: After Z score based thresholding, it may not be necessary to apply sorting-based thresholding to further select images. Alternatively, the user may opt to only apply sorting-based thresholding to obtain an image set, without using Z score based thresholding
9. Calibration curves generated using samples with the image selection model applied can be examined to determine their “goodness of fit”. Consult the fit statistics described under the heading “Curve fitting statistics” on the calibration curve page for more information.