Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917536200654848 |
|---|---|
| author | Tian, Yuan Li, Yue Xia, Wei Xu, Tianyu Zhang, Jian Shi, Liye Liu, Jing Wang, Yang Liu, Ming Xu, Qing Zhang, Yixuan He, Maggie M. He, Xiangjian |
| author_facet | Tian, Yuan Li, Yue Xia, Wei Xu, Tianyu Zhang, Jian Shi, Liye Liu, Jing Wang, Yang Liu, Ming Xu, Qing Zhang, Yixuan He, Maggie M. He, Xiangjian |
| contents | Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate determination of CSD outline and dimensions is crucial for treatment. However, CSDs are frequently overlooked by sonographers due to small size and irregular morphology, suboptimal image quality, and limited clinical awareness in resource-constrained settings. Despite artificial intelligence advances in medical imaging, no public dataset exists for transvaginal ultrasound CSD segmentation. To address this gap, we present a comprehensive CSD dataset comprising 1,111 images and 16 videos, yielding 501 positive samples with confirmed CSD and precise pixel-level manual annotations. Annotations are performed following standardized clinical guidelines through collaboration between experienced sonographers and trained PhD students. This work provides high-quality benchmark resources for advancing medical image segmentation algorithms and promoting clinical innovation. Ultimately, improved CSD diagnosis and subsequent treatment strategies can enhance the quality of life in women of reproductive age, representing significant value for both medical research and clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26774 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark Tian, Yuan Li, Yue Xia, Wei Xu, Tianyu Zhang, Jian Shi, Liye Liu, Jing Wang, Yang Liu, Ming Xu, Qing Zhang, Yixuan He, Maggie M. He, Xiangjian Computer Vision and Pattern Recognition Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate determination of CSD outline and dimensions is crucial for treatment. However, CSDs are frequently overlooked by sonographers due to small size and irregular morphology, suboptimal image quality, and limited clinical awareness in resource-constrained settings. Despite artificial intelligence advances in medical imaging, no public dataset exists for transvaginal ultrasound CSD segmentation. To address this gap, we present a comprehensive CSD dataset comprising 1,111 images and 16 videos, yielding 501 positive samples with confirmed CSD and precise pixel-level manual annotations. Annotations are performed following standardized clinical guidelines through collaboration between experienced sonographers and trained PhD students. This work provides high-quality benchmark resources for advancing medical image segmentation algorithms and promoting clinical innovation. Ultimately, improved CSD diagnosis and subsequent treatment strategies can enhance the quality of life in women of reproductive age, representing significant value for both medical research and clinical practice. |
| title | Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.26774 |