R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917516373131264 |
|---|---|
| author | Wang, Yuanhan Chen, Yifei Wu, Beining Liu, Mingxuan Hu, Xiaotian Jiang, Chunbo Li, Yijin Wang, Changmiao Qin, Feiwei Tian, Qiyuan |
| author_facet | Wang, Yuanhan Chen, Yifei Wu, Beining Liu, Mingxuan Hu, Xiaotian Jiang, Chunbo Li, Yijin Wang, Changmiao Qin, Feiwei Tian, Qiyuan |
| contents | Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensitive to imaging noise, boundary ambiguities, and the geometric amplification of local segmentation errors. We propose R2AoP, a reliable and robust AoP estimation framework that integrates structurally informed segmentation and confidence-guided geometric modeling to achieve stable and reproducible measurements. A three-branch local-structure-enhanced backbone improves the delineation of the pubic symphysis (PS) and fetal head (FH), while confidence-weighted contour fitting explicitly suppresses the influence of unreliable boundary points in AoP computation. To further improve performance under heterogeneous acquisition conditions, we introduce a lightweight geometry-reliable test-time adaptation strategy as an auxiliary component, enabling stable inference without target annotations. Extensive evaluations on multi-center benchmarks demonstrate consistent reductions in AoP error and boundary metrics compared with state-of-the-art AoP methods. Our source code is available at https://github.com/baiyou1234/R2AoP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21099 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound Wang, Yuanhan Chen, Yifei Wu, Beining Liu, Mingxuan Hu, Xiaotian Jiang, Chunbo Li, Yijin Wang, Changmiao Qin, Feiwei Tian, Qiyuan Computer Vision and Pattern Recognition Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensitive to imaging noise, boundary ambiguities, and the geometric amplification of local segmentation errors. We propose R2AoP, a reliable and robust AoP estimation framework that integrates structurally informed segmentation and confidence-guided geometric modeling to achieve stable and reproducible measurements. A three-branch local-structure-enhanced backbone improves the delineation of the pubic symphysis (PS) and fetal head (FH), while confidence-weighted contour fitting explicitly suppresses the influence of unreliable boundary points in AoP computation. To further improve performance under heterogeneous acquisition conditions, we introduce a lightweight geometry-reliable test-time adaptation strategy as an auxiliary component, enabling stable inference without target annotations. Extensive evaluations on multi-center benchmarks demonstrate consistent reductions in AoP error and boundary metrics compared with state-of-the-art AoP methods. Our source code is available at https://github.com/baiyou1234/R2AoP. |
| title | R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.21099 |