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文件名称：Topology-Preserving Deep Image Segmentation.pdf
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更新时间：2023-03-26 12:20:37
segmentation
Topology-Preserving Deep Image Segmentation
Segmentation algorithms are prone to make topological errors on fine-scale structures,
e.g., broken connections. We propose a novel method that learns to segment
with correct topology. In particular, we design a continuous-valued loss function
that enforces a segmentation to have the same topology as the ground truth, i.e.,
having the same Betti number. The proposed topology-preserving loss function
is differentiable and we incorporate it into end-to-end training of a deep neural
network. Our method achieves much better performance on the Betti number error,
which directly accounts for the topological correctness. It also performs superiorly
on other topology-relevant metrics, e.g., the Adjusted Rand Index and the Variation
of Information. We illustrate the effectiveness of the proposed method on a broad
spectrum of natural and biomedical datasets.