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Self-training boosts coronary vessel segmentation in X-ray angiography

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a self-training method to improve the segmentation of coronary vessels in X-ray angiography, particularly for data from centers not included in the initial training set. By using pseudo-labels generated from a pre-trained nnU-Net model and a pre-contrast frame subtraction technique, the models showed improved performance on external datasets like XCAD. The effectiveness of this self-training approach is highly dependent on the quality of the initial pseudo-labels generated by the model. AI

IMPACT Enhances medical imaging analysis by improving model generalization to new datasets without additional manual annotation.

RANK_REASON The cluster contains a research paper detailing a new method for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Self-training boosts coronary vessel segmentation in X-ray angiography

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The cluster contains a research paper detailing a new method for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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

    arXiv:2610.40302v1 Announce Type: cross Abstract: Deep learning models for coronary vessel segmentation in X-ray angiography often perform poorly on data from centres that were not represented in training. Apart from differences in equipment, the labels of the largest public data…

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