Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images

Choy, Kevin C.; Li, Guorong; Stamer, W. Daniel; Farsiu, Sina

doi:10.1016/j.exer.2021.108844

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…More recently, Choy et al. developed the first DL model to segment the Schlemm’s canal lumen on OCT in living mouse eyes ( 91 ). The budding use of AI in animal models of glaucoma is not limited to in vivo imaging.…”

Section: Discussionmentioning

confidence: 99%

“…In a recent study by Fuentes-Hurtado, et al a DenseNet CNN was further trained to discriminate between glaucoma and healthy eyes in OCT imaging acquired in a rodent (rat) model, achieving an AUC of 0.99 (90). More recently, Choy et al developed the first DL model to segment the Schlemm's canal lumen on OCT in living mouse eyes (91). The budding use of AI in animal models of glaucoma is not limited to in vivo imaging.…”

Section: Future Directions For Deep Learning Applications In Aging An...mentioning

confidence: 99%

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Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Falconi¹,

Sappington²

2022

Front. Ophthalmol.

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Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.

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Section: Discussionmentioning

confidence: 99%

Section: Future Directions For Deep Learning Applications In Aging An...mentioning

confidence: 99%

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Falconi¹,

Sappington²

2022

Front. Ophthalmol.

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“…Importantly, this model can become a useful area of analysis with recent AI developments in glaucoma animal models. In OCT images, Choy et al employed AI technology to delineate Schlemm’s canal lumens in mouse eyes ( 46 ). Similar segmentation and shape analysis approaches were recently applied to fixed tissues for automated analysis of multiple retinal morphological changes, including RNFL thickness ( 42 ), optic nerve density, and retinal ganglion cell soma density in animal models ( 47 , 48 ).…”

Section: Areas Of Research Opportunities For Reverse Translation Of A...mentioning

confidence: 99%

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Pasquale

Girard

et al. 2023

Front. Ophthalmol.

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Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

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CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network

Liang,

Zhang,

Wei

et al. 2024

Computerized Medical Imaging and Graphics

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Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images

Cited by 6 publications

References 51 publications

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network

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