.png){kind=icon}
.png){kind=icon}
Return
Semantic Segmentation with Spreading Scribbles
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-031-98694-9_6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Hand-annotating medical images with segmentation masks requires an immense amount of time and effort from clinical experts. Replacing full masks with a simpler annotating gesture can mitigate annotation costs. This can come in the form of a scribble, and leads to weakly supervised training scenarios. Scribble-supervised segmentation typically utilises advanced neural architectures to compensate for the limited training data. Instead of just relying strictly on the pixels from each scribble, we also enhance each scribble by spreading, i.e. propagating, annotation labels through the image. We use a hierarchical partitioning of the image, produced with watershed/waterfall transforms, and propagate the individual pixel labels through the waterfall regions. We propose that a semantic label can be propagated to all other pixels in the same waterfall region. This increases the number of pixels that can be used for training supervision. We show experimentally that this technique greatly boosts the performance of established neural architectures on public semantic segmentation datasets like ACDC and MSCMRseg.
Keywords:
Scribble-supervised segmentation
Waterfall transform
Spreading scribbles
Journal
M
IF:
0
Papers:
23
Citations:
0