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New STORK model automates uterine contraction detection in fetal MRI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed STORK, a novel multi-instance learning model designed to detect uterine contractions in fetal MRI scans. This model utilizes a weakly-supervised approach, trained on series-level labels rather than requiring frame-by-frame annotation. STORK decomposes spatio-temporal convolutions to efficiently capture tissue motion and displacement, achieving a series-level AUROC of 95.0% and AUPRC of 94.6% on a dataset of approximately 700 MRI series. The model's ability to identify predictive features beyond the placenta offers a new automated tool for analyzing uterine behavior. AI

IMPACT This model offers a new automated method for analyzing uterine behavior, potentially improving prenatal care and research.

RANK_REASON The item is a research paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

New STORK model automates uterine contraction detection in fetal MRI

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The item is a research paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Melissa Schween, Tristan Gottwald, Jordina Aviles Verdera, Lisa Story, Mary Rutherford, Jana Hutter ·

    STORK: Spatio-Temporal Observation of uterine contRactions via neural networKs

    arXiv:2610.09598v1 Announce Type: new Abstract: Uterine contractions in fetal MRI are typically identified manually and discarded, limiting insights into contraction dynamics. We formalize Uterine Contractile Activity Detection (UCAD) as a weakly-supervised learning problem and i…

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