ArticleBiomedical engineering online2026
A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.
Article in Biomedical engineering online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectTo develop and evaluate a multi-stage computer-aided determination (CAD) method for automated antral follicle count (AFC) and dominant follicle identification using pelvic magnetic resonance imaging (MRI), aiming to overcome the limitations of MRI-based follicular analysis, including high annotation cost and dependence on manual interpretation. MATERIALS AND
methodsA total of 417 T2-weighted MRI slices from 124 patients were selected by radiologists, ensuring ovarian visibility and excluding poor quality images. The proposed CAD method incorporates a You Only Look Once version 11 detection model, which accurately selects MRI slices containing ovarian structures. This is followed by LCR-UNet, a novel segmentation model that integrates a lightweight atrous spatial pyramid pooling module and a cascaded decoder (CR-decoder) with channel shuffle convolutional block and residual block.
resultsThe model achieved a Dice similarity coefficient of 0.8571 for follicle segmentation on MRI images. Based on pixel area ranking and ellipse fitting, dominant follicle identification reached an accuracy of 92.86% and an F1-score of 0.8929. Additionally, size-based filtering enabled automated counting of antral follicles with a precision of 82.91%. DISCUSSION: The proposed CAD method approaches the performance of experienced radiologists while reducing manual workload, offering a reliable tool for ovarian reserve and fertility assessment.
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