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The tumour was then automatically delineated using a 42% SUVmax threshold, and this delineation was used to assess the tumour volume [15].
First, geographical sub-areas were delineated using a parent lithology data layer.
This, together with evidence of strong correspondence among the networks defined by iFC and those delineated using a variety of other neuroimaging techniques, suggests a fundamental brain architecture detectable across multiple functional and structural imaging modalities.
Forest degradation due to logging was delineated using a 1 km buffer around identified logging roads.
An averaged image of the last three frames of the scan was produced and lesions were delineated using a 50% threshold of the peak standardised uptake value (SUVpeak).
For the data set of centre B, Tvol was delineated using a cut-off of the TBR > 1.6 based on a previous biopsy controlled study [8].
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Iceberg areas were delineated using an automated algorithm based on the contrast between the icebergs and the surrounding seawater.
Thirty seven clusters of epidemiologically linked premises were delineated using an interpolated surface of date of onset of clinical signs, geographic data and location of infected premises.
Significant uptake regions of PET M are delineated using an adaptive thresholding method (step 5).
Finally, volumes were delineated using an adaptive threshold based on the SBR PETSBRR), as developed at Université St. Luc in Brussels, Belgium [ 14].
In addition, two non-neocortical regions of the brain, namely the amygdala and the hippocampus, were automatically delineated using an algorithm that examines variations in voxel intensities and spatial relationships to classify non-neocortical regions on MRI scans (Fischl et al., 2002).
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