How does pylinac count ROI in the planar imaging module - las vegas

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Hello together,
I have asked another question about pylinac, but maybe it’s more structured when I open an own topic for this question. But I have asked it in the original thread as well.

The most important question to me is:
How does pylinac count ROI in the planar imaging module with the las vegas phantom?

There are 7 ROI “seen” and there are 7 ROI above the violet line in the graphic which represents my threshold - but which ROI in the right side of the plot of the analyze-method represent which ROI in the actual dicom-picture?

And why are there 7 out of 19 ROI seen but 20 (not 19) ROI are highlighted in the colored graphic on the left side of the plot?

I really don’t get the connection between the real dicom-image and the graph on the right side or the graphic on the left side of the plot of the analyze-method.
I just want to know which ROI are seen and maybe color just this ROI green in the graphic.

Maybe I don’t see the forest for the trees…
ANY help is appreciated. Attached a screenshot for better understanding.

Thanks a lot

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2

Perhaps this will help to identify regions:

import matplotlib
from matplotlib import pyplot as plt
from pylinac import LasVegas

lasvegas = LasVegas.from_demo_image()
lasvegas.analyze()

fig, ax = plt.subplots()
ax.imshow(lasvegas.image.array, cmap=matplotlib.cm.get_cmap(“prism”), interpolation=“none”, aspect=“equal”, origin=‘upper’)

plot the background ROIS

for roi in lasvegas.low_contrast_background_rois:
roi.plot2axes(ax, edgecolor=‘yellow’)
ax.text(roi.center.x, roi.center.y ,“B”, horizontalalignment=‘center’, verticalalignment=‘center’)

low contrast ROIs

for ind, roi in enumerate(lasvegas.low_contrast_rois):
roi.plot2axes(ax, edgecolor=roi.plot_color)
ax.text(roi.center.x, roi.center.y ,str(ind), horizontalalignment=‘center’, verticalalignment=‘center’)

plt.show()