Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918436650614784 |
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| author | Fati, Francesca Rota, Alberto Gregory, Adriana V. Catozzo, Anna Giuliano, Maria C. Dhar, Mrinal De Vitis, Luigi Packard, Annie T. Multinu, Francesco De Momi, Elena Langstraat, Carrie L. Kline, Timothy L. |
| author_facet | Fati, Francesca Rota, Alberto Gregory, Adriana V. Catozzo, Anna Giuliano, Maria C. Dhar, Mrinal De Vitis, Luigi Packard, Annie T. Multinu, Francesco De Momi, Elena Langstraat, Carrie L. Kline, Timothy L. |
| contents | Adnexal mass evaluation via ultrasound is a challenging clinical task, often hindered by subjective interpretation and significant inter-observer variability. While automated segmentation is a foundational step for quantitative risk assessment, traditional fully supervised convolutional architectures frequently require large amounts of pixel-level annotations and struggle with domain shifts common in medical imaging. In this work, we propose a label-efficient segmentation framework that leverages the robust semantic priors of a pretrained DINOv3 foundational vision transformer backbone. By integrating this backbone with a Dense Prediction Transformer (DPT)-style decoder, our model hierarchically reassembles multi-scale features to combine global semantic representations with fine-grained spatial details. Evaluated on a clinical dataset of 7,777 annotated frames from 112 patients, our method achieves state-of-the-art performance compared to established fully supervised baselines, including U-Net, U-Net++, DeepLabV3, and MAnet. Specifically, we obtain a Dice score of 0.945 and improved boundary adherence, reducing the 95th-percentile Hausdorff Distance by 11.4% relative to the strongest convolutional baseline. Furthermore, we conduct an extensive efficiency analysis demonstrating that our DINOv3-based approach retains significantly higher performance under data starvation regimes, maintaining strong results even when trained on only 25% of the data. These results suggest that leveraging large-scale self-supervised foundations provides a promising and data-efficient solution for medical image segmentation in data-constrained clinical environments. Project Repository: https://github.com/FrancescaFati/MESA |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_08045 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images Fati, Francesca Rota, Alberto Gregory, Adriana V. Catozzo, Anna Giuliano, Maria C. Dhar, Mrinal De Vitis, Luigi Packard, Annie T. Multinu, Francesco De Momi, Elena Langstraat, Carrie L. Kline, Timothy L. Computer Vision and Pattern Recognition Adnexal mass evaluation via ultrasound is a challenging clinical task, often hindered by subjective interpretation and significant inter-observer variability. While automated segmentation is a foundational step for quantitative risk assessment, traditional fully supervised convolutional architectures frequently require large amounts of pixel-level annotations and struggle with domain shifts common in medical imaging. In this work, we propose a label-efficient segmentation framework that leverages the robust semantic priors of a pretrained DINOv3 foundational vision transformer backbone. By integrating this backbone with a Dense Prediction Transformer (DPT)-style decoder, our model hierarchically reassembles multi-scale features to combine global semantic representations with fine-grained spatial details. Evaluated on a clinical dataset of 7,777 annotated frames from 112 patients, our method achieves state-of-the-art performance compared to established fully supervised baselines, including U-Net, U-Net++, DeepLabV3, and MAnet. Specifically, we obtain a Dice score of 0.945 and improved boundary adherence, reducing the 95th-percentile Hausdorff Distance by 11.4% relative to the strongest convolutional baseline. Furthermore, we conduct an extensive efficiency analysis demonstrating that our DINOv3-based approach retains significantly higher performance under data starvation regimes, maintaining strong results even when trained on only 25% of the data. These results suggest that leveraging large-scale self-supervised foundations provides a promising and data-efficient solution for medical image segmentation in data-constrained clinical environments. Project Repository: https://github.com/FrancescaFati/MESA |
| title | Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.08045 |