Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation
Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentati...
- Autores:
-
Sanchez, Sergio
Vallez, Noelia
Bueno, Gloria
Marrugo, Andres G
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12773
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12773
- Palabra clave:
- Data augmentation
Warping transforms
Corneal endothelium
Semi-supervised segmentation
Deep convolutional neural networks (CNNs)
Image segmentation
Medical imaging
Keypoint extraction
Delaunay triangulation
Affine transformations
Mask refinement Mean intersection over union (mIoU)
Dice coefficient (DC)
Natural variability
Medical image cell segmentation
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| title |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| spellingShingle |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation Data augmentation Warping transforms Corneal endothelium Semi-supervised segmentation Deep convolutional neural networks (CNNs) Image segmentation Medical imaging Keypoint extraction Delaunay triangulation Affine transformations Mask refinement Mean intersection over union (mIoU) Dice coefficient (DC) Natural variability Medical image cell segmentation LEMB |
| title_short |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| title_full |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| title_fullStr |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| title_full_unstemmed |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| title_sort |
Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation |
| dc.creator.fl_str_mv |
Sanchez, Sergio Vallez, Noelia Bueno, Gloria Marrugo, Andres G |
| dc.contributor.author.none.fl_str_mv |
Sanchez, Sergio Vallez, Noelia Bueno, Gloria Marrugo, Andres G |
| dc.subject.keywords.spa.fl_str_mv |
Data augmentation Warping transforms Corneal endothelium Semi-supervised segmentation Deep convolutional neural networks (CNNs) Image segmentation Medical imaging Keypoint extraction Delaunay triangulation Affine transformations Mask refinement Mean intersection over union (mIoU) Dice coefficient (DC) Natural variability Medical image cell segmentation |
| topic |
Data augmentation Warping transforms Corneal endothelium Semi-supervised segmentation Deep convolutional neural networks (CNNs) Image segmentation Medical imaging Keypoint extraction Delaunay triangulation Affine transformations Mask refinement Mean intersection over union (mIoU) Dice coefficient (DC) Natural variability Medical image cell segmentation LEMB |
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LEMB |
| description |
Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-11-29T19:18:30Z |
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2024-11-29T19:18:30Z |
| dc.date.issued.none.fl_str_mv |
2024-11-12 |
| dc.date.submitted.none.fl_str_mv |
2024-11-29 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_6501 |
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Sanchez S, Vallez N, Bueno G, Marrugo AG (2024) Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation. PLoS ONE 19(11): e0311849. https://doi.org/10.1371/journal.pone.0311849 |
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https://hdl.handle.net/20.500.12585/12773 |
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10.1371/journal.pone.0311849 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Sanchez S, Vallez N, Bueno G, Marrugo AG (2024) Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation. PLoS ONE 19(11): e0311849. https://doi.org/10.1371/journal.pone.0311849 10.1371/journal.pone.0311849 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12773 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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18 páginas |
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Sanchez, Sergio6600d51e-4c2e-4d6c-9bc8-2713e182f4faVallez, Noeliaa89e2d17-4098-4a84-b70f-d77ea4d6dbd2Bueno, Gloriaf5a952f3-894a-425f-b496-ce94116482ebMarrugo, Andres G3d6cd388-d48f-4669-934f-49ca4179f5422024-11-29T19:18:30Z2024-11-29T19:18:30Z2024-11-122024-11-29Sanchez S, Vallez N, Bueno G, Marrugo AG (2024) Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation. PLoS ONE 19(11): e0311849. https://doi.org/10.1371/journal.pone.0311849https://hdl.handle.net/20.500.12585/1277310.1371/journal.pone.0311849Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarImage segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks.Minciencias18 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Plos OneData augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Data augmentationWarping transformsCorneal endotheliumSemi-supervised segmentationDeep convolutional neural networks (CNNs)Image segmentationMedical imagingKeypoint extractionDelaunay triangulationAffine transformationsMask refinement Mean intersection over union (mIoU)Dice coefficient (DC)Natural variabilityMedical image cell segmentationLEMBCartagena de IndiasIngenieríaInvestigadoresFabijańska A. Automatic segmentation of corneal endothelial cells from microscopy images. Biomedical Signal Processing and Control. 2019; 47:145–158. https://doi.org/10.1016/j.bspc.2018.08.018Selig B, Vermeer KA, Rieger B, Hillenaar T, Luengo Hendriks CL. Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC medical imaging. 2015; 15:1–15. https://doi. org/10.1186/s12880-015-0054-3 PMID: 25928199. Okumura N, Yamada S, Nishikawa T, Narimoto K, Okamura K, Izumi A, et al. U-Net Convolutional Neural Network for Segmenting the Corneal Endothelium in a Mouse Model of Fuchs Endothelial Corneal Dystrophy. Cornea. 2022; 41(7):901–907. https://doi.org/10.1097/ICO.0000000000002956 PMID: 34864800Fabijańska A. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network. Artificial Intelligence in Medicine. 2018; 88:1–13. https://doi.org/10.1016/j.artmed.2018.04.004 PMID: 29680687Scarpa F, Ruggeri A. Development of a reliable automated algorithm for the morphometric analysis of human corneal endothelium. Cornea. 2016; 35(9):1222–1228. https://doi.org/10.1097/ICO. 0000000000000908 PMID: 27310881Prada AM, Quintero F, Mendoza K, Galvis V, Tello A, Romero LA, et al. Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence–Derived Morphometric Parameters From Specular Microscopy Images. Cornea. 2024; 43(9). https://doi.org/10.1097/ICO.0000000000003460 PMID: 38334475Patel DV, McGhee CN. Quantitative analysis of in vivo confocal microscopy images: a review. Survey of ophthalmology. 2013; 58(5):466–475. https://doi.org/10.1016/j.survophthal.2012.12.003 PMID: 23453401Lan G, Twa MD, Song C, Feng J, Huang Y, Xu J, et al. In vivo corneal elastography: A topical review of challenges and opportunities. Computational and Structural Biotechnology Journal. 2023;. https://doi. org/10.1016/j.csbj.2023.04.009 PMID: 37181662Shen Z, Fu H, Shen J, Shao L. Modeling and Enhancing Low-Quality Retinal Fundus Images. IEEE Transactions on Medical Imaging. 2020;PP:1–1. https://doi.org/10.1109/TMI.2020.3043495Soh YQ, Peh GS, Naso SL, Kocaba V, Mehta JS. Automated clinical assessment of corneal guttae in fuchs endothelial corneal dystrophy. American Journal of Ophthalmology. 2021; 221:260–272. https:// doi.org/10.1016/j.ajo.2020.07.029 PMID: 32730910Aquino NR, Gutoski M, Hattori LT, Lopes HS. The effect of data augmentation on the performance of convolutional neural networks. Braz Soc Comput Intell. 2017;Ginsburger K. Style Augmentation improves Medical Image Segmentation; 2022. https://doi.org/10. 1111/1468-4446.12970 PMID: 35855502Vallez N, Bueno G, Deniz O, Blanco S. Diffeomorphic transforms for data augmentation of highly variable shape and texture objects. Computer Methods and Programs in Biomedicine. 2022; 219:106775. https://doi.org/10.1016/j.cmpb.2022.106775 PMID: 35397412. Araslanov N, Roth S. 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Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy. Translational Vision Science & Technology. 2021; 10 (13):27–27. https://doi.org/10.1167/tvst.10.13.27 PMID: 34807254Vigueras-Guille´n JP, Lasenby J, Seeliger F. Rotaflip: A New CNN Layer for Regularization and Rotational Invariance in Medical Images; 2021.Vigueras-Guille´n J, Sari B, Goes S, Lemij H, Rooij J, Vermeer K, et al. Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation. BMC Biomedical Engineering. 2019; 1. https://doi.org/10.1186/s42490-019-0003-2 PMID: 32903308Wu J, Shen B, Zhang H, Wang J, Pan Q, Huang J, et al. Semi-supervised Learning for Nerve Segmentation in Corneal Confocal Microscope Photography. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV. Berlin, Heidelberg: Springer-Verlag; 2022. p. 47–57.Sanchez S, Mendoza K, Quintero F, Prada AM, Tello A, Galvis V, et al. Deep neural networks for evaluation of specular microscopy images of the corneal endothelium with Fuchs’ dystrophy. In: Pattern Recognition and Tracking XXXIV. vol. 12527. SPIE; 2023. p. 183–191.Kucharski A, Fabijańska A. Corneal endothelial image segmentation training data generation using GANs. Do experts need to annotate? Biomedical Signal Processing and Control. 2023; 85:104985. https://doi.org/10.1016/j.bspc.2023.104985Saxena D, Cao J. Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions; 2023. Qu JH, Qin XR, Peng RM, Xiao GG, Cheng J, Gu SF, et al. A Fully Automated Segmentation and Morphometric Parameter Estimation System for Assessing Corneal Endothelial Cell Images. American Journal of Ophthalmology. 2022; 239:142–153. https://doi.org/10.1016/j.ajo.2022.02.026 PMID: 35288075Zbontar J, Jing L, Misra I, LeCun Y, Deny S. Barlow twins: Self-supervised learning via redundancy reduction. In: International Conference on Machine Learning. PMLR; 2021. p. 12310–12320.He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition; 2015.. Lee Y, Yim B, Kim H, Park E, Cui X, Woo T, et al. Wide-Residual-Inception Networks for Real-time Object Detection; 2017Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks; 2018Chen X, Hsieh CJ, Gong B. When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations; 2022.Shafiq M, Gu Z. Deep Residual Learning for Image Recognition: A Survey. Applied Sciences. 2022; 12 (18). https://doi.org/10.3390/app12188972Chen Y, Zheng H, Ma Y, Yan Z. Image stitching based on angle-consistent warping. Pattern Recognition. 2021; 117:107993. https://doi.org/10.1016/j.patcog.2021.107993Kulwa F, Li C, Grzegorzek M, Rahaman MM, Shirahama K, Kosov S. 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