Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21575 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909926789480448 |
|---|---|
| author | Mo, Chou Suh, Yehyun Martin, J. Ryan Moyer, Daniel |
| author_facet | Mo, Chou Suh, Yehyun Martin, J. Ryan Moyer, Daniel |
| contents | Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss Mo, Chou Suh, Yehyun Martin, J. Ryan Moyer, Daniel Computer Vision and Pattern Recognition Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable. |
| title | Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.21575 |