Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909594665615360 |
|---|---|
| author | Nahass, George R. Koehler, Emma Tomaras, Nicholas Lopez, Danny Cheung, Madison Palacios, Alexander Peterson, Jeffrey C. Hubschman, Sasha Green, Kelsey Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin |
| author_facet | Nahass, George R. Koehler, Emma Tomaras, Nicholas Lopez, Danny Cheung, Madison Palacios, Alexander Peterson, Jeffrey C. Hubschman, Sasha Green, Kelsey Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin |
| contents | Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications Nahass, George R. Koehler, Emma Tomaras, Nicholas Lopez, Danny Cheung, Madison Palacios, Alexander Peterson, Jeffrey C. Hubschman, Sasha Green, Kelsey Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin Computer Vision and Pattern Recognition Tissues and Organs Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community. |
| title | Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications |
| topic | Computer Vision and Pattern Recognition Tissues and Organs |
| url | https://arxiv.org/abs/2409.20407 |