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arXiv Computer Vision By Juan Miguel Valverde, Maja {\O}stergaard, Adrian Rodriguez-Palomo, Peter Alling Strange Vibe, Nina K{\o}lln Wittig, Henrik Birkedal, Anders Bjorholm Dahl

Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation

Read the original on arXiv Computer Vision →

The paper introduces CoLeTra, a data augmentation technique designed to enhance topology accuracy in image segmentation of thin, tubular structures such as blood vessels. By artificially breaking these structures in training images while preserving the original labels, CoLeTra teaches models that continuous structures can appear disconnected, improving segmentation quality even when training labels are topologically inaccurate. Experiments across various architectures, loss functions, and datasets show that CoLeTra yields more topologically accurate segmentations and often boosts Dice and clDice scores, and the authors also release a dedicated dataset for topology-focused segmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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