Automatic detection of pupil reactions in cataract surgery videos.
In the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeri...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258390&type=printable |
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| author | Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi |
| author_facet | Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi |
| author_sort | Natalia Sokolova |
| collection | DOAJ |
| description | In the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeries to detect even minor intraoperative deviations that might explain a lack of a post-operative success becomes more and more important. Up-to-now surgical videos are evaluated by just looking at a very limited number of intraoperative data sets, or as done in studies evaluating the pupil changes that occur during surgeries, in a small number intraoperative picture only. A continuous measurement of pupil changes over the whole surgery, that would achieve clinically more relevant data, has not yet been described. Therefore, the automatic retrieval of such events may be a great support for a post-operative analysis. This would be especially true if large data files could be evaluated automatically. In this work, we automatically detect pupil reactions in cataract surgery videos. We employ a Mask R-CNN architecture as a segmentation algorithm to segment the pupil and iris with pixel-based accuracy and then track their sizes across the entire video. We can detect pupil reactions with a harmonic mean (H) of Recall, Precision, and Ground Truth Coverage Rate (GTCR) of 60.9% and average prediction length (PL) of 18.93 seconds. However, we consider the best configuration for practical use the one with the H value of 59.4% and PL of 10.2 seconds, which is much shorter. We further investigate the generalization ability of this method on a slightly different dataset without retraining the model. In this evaluation, we achieve the H value of 49.3% with the PL of 18.15 seconds. |
| format | Article |
| id | doaj-art-306a2fba309145edb84b54f015396c2c |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-306a2fba309145edb84b54f015396c2c2025-08-20T02:33:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011610e025839010.1371/journal.pone.0258390Automatic detection of pupil reactions in cataract surgery videos.Natalia SokolovaKlaus SchoeffmannMario TaschwerStephanie SarnyDoris Putzgruber-AdamitschYosuf El-ShabrawiIn the light of an increased use of premium intraocular lenses (IOL), such as EDOF IOLs, multifocal IOLs or toric IOLs even minor intraoperative complications such as decentrations or an IOL tilt, will hamper the visual performance of these IOLs. Thus, the post-operative analysis of cataract surgeries to detect even minor intraoperative deviations that might explain a lack of a post-operative success becomes more and more important. Up-to-now surgical videos are evaluated by just looking at a very limited number of intraoperative data sets, or as done in studies evaluating the pupil changes that occur during surgeries, in a small number intraoperative picture only. A continuous measurement of pupil changes over the whole surgery, that would achieve clinically more relevant data, has not yet been described. Therefore, the automatic retrieval of such events may be a great support for a post-operative analysis. This would be especially true if large data files could be evaluated automatically. In this work, we automatically detect pupil reactions in cataract surgery videos. We employ a Mask R-CNN architecture as a segmentation algorithm to segment the pupil and iris with pixel-based accuracy and then track their sizes across the entire video. We can detect pupil reactions with a harmonic mean (H) of Recall, Precision, and Ground Truth Coverage Rate (GTCR) of 60.9% and average prediction length (PL) of 18.93 seconds. However, we consider the best configuration for practical use the one with the H value of 59.4% and PL of 10.2 seconds, which is much shorter. We further investigate the generalization ability of this method on a slightly different dataset without retraining the model. In this evaluation, we achieve the H value of 49.3% with the PL of 18.15 seconds.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258390&type=printable |
| spellingShingle | Natalia Sokolova Klaus Schoeffmann Mario Taschwer Stephanie Sarny Doris Putzgruber-Adamitsch Yosuf El-Shabrawi Automatic detection of pupil reactions in cataract surgery videos. PLoS ONE |
| title | Automatic detection of pupil reactions in cataract surgery videos. |
| title_full | Automatic detection of pupil reactions in cataract surgery videos. |
| title_fullStr | Automatic detection of pupil reactions in cataract surgery videos. |
| title_full_unstemmed | Automatic detection of pupil reactions in cataract surgery videos. |
| title_short | Automatic detection of pupil reactions in cataract surgery videos. |
| title_sort | automatic detection of pupil reactions in cataract surgery videos |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258390&type=printable |
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