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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.10593 |
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| _version_ | 1866909785844088832 |
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| author | McDonald-Bowyer, Aoife Wijekoon, Anjana Love, Ryan Laurance Allan, Katie Colvin, Scott Gentry-Maharaj, Aleksandra Olaitan, Adeola Stoyanov, Danail Stilli, Agostino Bano, Sophia |
| author_facet | McDonald-Bowyer, Aoife Wijekoon, Anjana Love, Ryan Laurance Allan, Katie Colvin, Scott Gentry-Maharaj, Aleksandra Olaitan, Adeola Stoyanov, Danail Stilli, Agostino Bano, Sophia |
| contents | Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-resource settings, but require reliable visual guidance. This study evaluates deep learning methods for real-time segmentation of the cervical os in transvaginal endoscopic images. Five encoder-decoder architectures were compared using 913 frames from 200 cases in the IARC Cervical Image Dataset, annotated by gynaecologists. Performance was assessed using IoU, DICE, detection rate, and distance metrics with ten-fold cross-validation. EndoViT/DPT, a vision transformer pre-trained on surgical video, achieved the highest DICE (0.50 \pm 0.31) and detection rate (0.87 \pm 0.33), outperforming CNN-based approaches. External validation with phantom data demonstrated robust segmentation under variable conditions at 21.5 FPS, supporting real-time feasibility. These results establish a foundation for integrating automated os recognition into speculum-free cervical screening devices to support non-expert use in both high- and low-resource contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening McDonald-Bowyer, Aoife Wijekoon, Anjana Love, Ryan Laurance Allan, Katie Colvin, Scott Gentry-Maharaj, Aleksandra Olaitan, Adeola Stoyanov, Danail Stilli, Agostino Bano, Sophia Image and Video Processing Computer Vision and Pattern Recognition Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-resource settings, but require reliable visual guidance. This study evaluates deep learning methods for real-time segmentation of the cervical os in transvaginal endoscopic images. Five encoder-decoder architectures were compared using 913 frames from 200 cases in the IARC Cervical Image Dataset, annotated by gynaecologists. Performance was assessed using IoU, DICE, detection rate, and distance metrics with ten-fold cross-validation. EndoViT/DPT, a vision transformer pre-trained on surgical video, achieved the highest DICE (0.50 \pm 0.31) and detection rate (0.87 \pm 0.33), outperforming CNN-based approaches. External validation with phantom data demonstrated robust segmentation under variable conditions at 21.5 FPS, supporting real-time feasibility. These results establish a foundation for integrating automated os recognition into speculum-free cervical screening devices to support non-expert use in both high- and low-resource contexts. |
| title | Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.10593 |