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arXiv Computer Vision By James Willoughby, Irina Voiculescu

FPG-Seg: Annotation-free Retinal Vessel Topology Segmentation

Read the original on arXiv Computer Vision →

FPG-Seg is an annotation‑free pipeline for retinal vessel segmentation that combines classical vesselness filtering, Bayesian deep learning, and topology‑aware graph reconstruction. It achieves a clDice score of 0.74–0.78, which is 86–98% of the performance of a fully‑supervised baseline, across four benchmark datasets. The work also presents the first performance benchmark of an annotation‑free vessel segmentation method on the FIVES dataset.

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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