Simplify overlay_segmentations using MergeLabelMap + LabelMapContourOverlay - #513
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…verlay Replace the per-segmentation LabelMapContourOverlay + manual mask compositing (built on BinaryProjectionImageFilter/ToScalarImage) with a single MergeLabelMapFilter + LabelMapContourOverlay call. Each binary segmentation is cast to a label map (all label 1); MergeLabelMapFilter's default KEEP method keeps each input's region distinct despite the shared label value, so overlapping contours are handled correctly in one pass instead of manual per-segmentation compositing.
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Summary
Simplifies
overlay_segmentationsinPython/05_Results_Visualization.ipynb.Replaces the per-segmentation
LabelMapContourOverlaycalls plus manual maskcompositing (built on
BinaryProjectionImageFilter/ToScalarImage) with asingle
MergeLabelMapFilter+LabelMapContourOverlaycall:sitkLabelUInt8,all sharing label value
1).MergeLabelMapFilter's defaultKeepmethod keeps each input's regiondistinct despite the shared label value, so contours for all segmentations
(including overlapping ones) are drawn correctly in a single
LabelMapContourOverlaycall, instead of manually compositing per-segmentationresults with a projection-based mask.
Verified with a standalone reproduction (two overlapping binary masks) that this
produces pixel-identical output to explicitly scaling each segmentation to a
unique label id before merging, and confirmed the notebook re-executes end-to-end
with
jupyter nbconvert --executewith no errors.