Pradeep Reddy Raamana
Pradeep Reddy Raamana
just because one ROI was erroneous (in few vertices or more) in a particular subject's parcellation doesn;t mean we need to discard the entire parcellation.. Hence, after a dataset or...
when reviewing a large dataset, it maybe useful under some circumstances, to allow the user to focus on "outlier" candidates only, reducing the size by 90% or so, depending on...
it would be very useful to implement domain- or project- or user-specific rules for outlier alerts.. one example could be: hippocampal volume being close to 0mm^3 or 10000mm^3 is an...
It would be very useful to automatic record location (image coordinates) or ROI name (parcel annotation perhaps?) of erroneous areas as users identify them. They can note it in Notes...
for easier programmatic subject-wise access
Not everyone perceive red and green accurately, so make the mixing accessible, let users customize it to whatever Colors they like!
- ideal sequence: start with external surface visualizations only (perhaps 2 rows of 3 panels large size), then cross-sectional internal view, and then a rating or a keyboard shortcut to...
an option to specify a “negative” mask (lesion, pathology, bad FOV etc) would be useful. VisualQC can use such a mask to highlight erroneous areas and parcels during the review,...
Initial draft of a simple tool to check alignment and contrast in MT sat images
On some MRI scans, some light colors such as blue etc are very difficult to resolve, making it difficult to judge the accuracy of the parcellation. An option, such as...