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arXiv AI By Danial Moafi, Aida Moafi, Mostafa Aryafar, Ashkan Sami, Maryam Aryafar

Self-training for cross-centre coronary vessel segmentation in X-ray angiography using pre-contrast frame subtraction

Read the original on arXiv AI →

The study investigates self‑training for coronary vessel segmentation in X‑ray angiography, using pseudo‑labels from an nnU‑Net model trained on the ARCADE dataset and a contrast map derived from pre‑contrast frame subtraction. Six models were evaluated on ARCADE and two external datasets (XCAD and XACV); first‑round self‑training improved performance over baseline and a fine‑tuned control, with the XCAD model achieving a 2.7‑point Dice score increase. A second self‑training round yielded no further gains, and the benefit of self‑training was found to depend largely on the sensitivity of the initial pseudo‑label generator.

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 AI.

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