Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910601813426176 |
|---|---|
| author | Rasheed, Hassan Dorent, Reuben Fehrentz, Maximilian Kapur, Tina Wells III, William M. Golby, Alexandra Frisken, Sarah Schnabel, Julia A. Haouchine, Nazim |
| author_facet | Rasheed, Hassan Dorent, Reuben Fehrentz, Maximilian Kapur, Tina Wells III, William M. Golby, Alexandra Frisken, Sarah Schnabel, Julia A. Haouchine, Nazim |
| contents | We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound Rasheed, Hassan Dorent, Reuben Fehrentz, Maximilian Kapur, Tina Wells III, William M. Golby, Alexandra Frisken, Sarah Schnabel, Julia A. Haouchine, Nazim Computer Vision and Pattern Recognition We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average. |
| title | Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.08169 |