Extend resegmenting functionality #420
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This PR now also contains some updates for the provenance info and the behavior in case of failing checks: Instead of logging an error and returning an empty feature vector when checks fail, raise a ValueError detailing the cause of the failing check. |
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In addition to the original mode (now default mode "absolute"), add new modes "relative" and "sigma", which allow for a dynamic threshold defined as either a threshold relative to the maximum value found in the ROI ("relative") or as a n * sigma difference from the mean ("sigma").
Additionally, it is now allowed to provide either 1 or 2 thresholds, where in case of 1 threshold it is interpreted as the lower threshold (i.e. retaining everything equal to or higher than that threshold).
In case the function fails, a ValueError is raised. The checks to determine the validity of the resegmented mask is not determined in this function, but is applied in the pipeline defined by featureextractor.
Update the general info class, which will now be updated at multiple points in the extraction pipeline. This enables reflecting the various changes introduced by the pre-processing. Instead of returning "None" when checks fail, raise a ValueError. This removes the need for checking for "None" and ensures that the extraction is cancelled, with a warning message shown to the user.
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In addition to the original mode (now default mode "absolute"), add new modes "relative" and "sigma", which allow for a dynamic threshold defined as either a threshold relative to the maximum value found in the ROI ("relative") or as a n * sigma difference from the mean ("sigma").
Additionally, it is now allowed to provide either 1 or 2 thresholds, where in case of 1 threshold it is interpreted as the lower threshold (i.e. retaining everything equal to or higher than that threshold).
In case the function fails, a ValueError is raised. The checks to determine the validity of the resegmented mask is not determined in this function, but is applied in the pipeline defined by featureextractor.