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Segmentation benchmark
Senretsu
Comparing segmentation models on thin structures, where connectivity matters more than overlap.
- annotated images
- 11k+annotated images
- model families compared
- 3model families compared
The problem
Standard segmentation losses over-dilate thin structures and break their connectivity, which ruins downstream measurement.
What I built
- 1
Benchmarked U-Net, YOLOv8-seg and Mask R-CNN on more than 11,000 annotated images.
- 2
Integrated the clDice loss into U-Net to reduce over-dilation and preserve connectivity.
Outcome
A reproducible comparison showing how loss design changes the topology of thin-structure predictions.
Looking for an intern with this profile?
I am available for a 6-month internship from February 2027. Write to me and I will send you my CV and answer within 48 hours.
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