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Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes

ICML 2023 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add Betti matching loss on top of Dice losssegmentation network · CREMIBetti matching error τ^err: 74.82 -> 64.9001Helped
add Betti matching loss on top of Dice losssegmentation network · RoadsBetti matching error τ^err: 58.90 -> 41.50Helped
add Betti matching loss on top of Dice losssegmentation network · synMnistBetti matching error τ^err: 1.849 -> 1.140Helped
add Betti matching loss on top of Dice losssegmentation network · BuildingsBetti matching error τ^err: Hu et al. 278.30 -> 244.58Helped
add Betti matching loss on top of Dice losssegmentation network · ColonBetti matching error τ^err: Hu et al. 17.25 vs Ours 16.00Mixed
increase alpha for Betti matching loss (ablation)Roads (Betti matching loss) · RoadsBetti matching error τ^err: 53.958 at alpha 0.0005 -> 41.50 at alpha 0.5Helped
increase alpha for Betti matching loss (ablation)synMnist (Betti matching loss) · synMnistDice: 0.871 at alpha 0.005 -> 0.796 at alpha 0.5Hurt
match both foreground and background levels (bothlevel) instead of foreground only (superlevel) (ablation)Betti matching loss · ElegansBetti matching error τ^err: 2.15 -> 1.70Helped
match both foreground and background levels (bothlevel) instead of foreground only (superlevel) (ablation)Betti matching loss · CREMIBetti matching error τ^err: 59.20 (superlevel) vs 60.48 (bothlevel)No clear effect
add image boundary (relative Betti matching) (ablation)Betti matching loss · RoadsBetti matching error τ^err: 41.500 relative vs 41.417 non-relative at alpha 0.5No clear effect
apply Betti matching loss to Hu et al. method (dim-0,1 matching instead of dim-1 only) (ablation)Hu et al. method · RoadsBetti matching error τ^err: dim-1 alpha 0.5: 58.583 vs dim-0,1 alpha 0.5: 44.708Mixed
train Betti matching loss from scratch vs pretrained (ablation)Betti matching loss · CREMIBetti matching error τ^err: qualitative claim onlyNo clear effect

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