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ZScribbleSeg framework uses efficient scribble annotations for medical image segmentation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ZScribbleSeg, a new framework designed to improve medical image segmentation using efficient scribble annotations. This approach addresses the labor-intensive nature of fully annotating datasets by maximizing the supervision derived from limited scribble input. ZScribbleSeg incorporates spatial relationships and shape constraints, utilizing an EM algorithm for accurate class ratio estimation, and has demonstrated competitive performance across six different segmentation tasks. AI

IMPACT Offers a more efficient method for medical image segmentation, potentially reducing annotation costs and improving model accuracy.

RANK_REASON This is a research paper detailing a new framework for image segmentation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ZScribbleSeg framework uses efficient scribble annotations for medical image segmentation

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ke Zhang, Bomin Wang, Hangqi Zhou, Xiahai Zhuang ·

    ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision

    arXiv:2605.06266v1 Announce Type: new Abstract: Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing s…

  2. arXiv cs.CV TIER_1 English(EN) · Xiahai Zhuang ·

    ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision

    Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing solutions mainly compute losses on annotated area…

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