Bibliografías temáticas / Glaucoma Optic nerve Machine learning

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Literatura académica sobre el tema "Glaucoma Optic nerve Machine learning"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 13 de febrero de 2022

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Artículos de revistas sobre el tema "Glaucoma Optic nerve Machine learning"

Barella, Kleyton Arlindo, Vital Paulino Costa, Vanessa Gonçalves Vidotti, Fabrício Reis Silva, Marcelo Dias y Edson Satoshi Gomi. "Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT". Journal of Ophthalmology 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/789129.

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Purpose. To investigate the diagnostic accuracy of machine learning classifiers (MLCs) using retinal nerve fiber layer (RNFL) and optic nerve (ON) parameters obtained with spectral domain optical coherence tomography (SD-OCT).Methods. Fifty-seven patients with early to moderate primary open angle glaucoma and 46 healthy patients were recruited. All 103 patients underwent a complete ophthalmological examination, achromatic standard automated perimetry, and imaging with SD-OCT. Receiver operating characteristic (ROC) curves were built for RNFL and ON parameters. Ten MLCs were tested. Areas under ROC curves (aROCs) obtained for each SD-OCT parameter and MLC were compared.Results. The mean age was56.5±8.9years for healthy individuals and59.9±9.0years for glaucoma patients (P=0.054). Mean deviation values were −1.4 dB for healthy individuals and −4.0 dB for glaucoma patients (P<0.001). SD-OCT parameters with the greatest aROCs were cup/disc area ratio (0.846) and average cup/disc (0.843). aROCs obtained with classifiers varied from 0.687 (CTREE) to 0.877 (RAN). The aROC obtained with RAN (0.877) was not significantly different from the aROC obtained with the best single SD-OCT parameter (0.846) (P=0.542).Conclusion. MLCs showed good accuracy but did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.

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Sułot, Dominika, David Alonso-Caneiro, Paweł Ksieniewicz, Patrycja Krzyzanowska-Berkowska y D. Robert Iskander. "Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method". PLOS ONE 16, n.º 6 (4 de junio de 2021): e0252339. http://dx.doi.org/10.1371/journal.pone.0252339.

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This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness—a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.

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An, Guangzhou, Kazuko Omodaka, Kazuki Hashimoto, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Hideo Yokota, Masahiro Akiba y Toru Nakazawa. "Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images". Journal of Healthcare Engineering 2019 (18 de febrero de 2019): 1–9. http://dx.doi.org/10.1155/2019/4061313.

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This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

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Abidi, Syed S. R., Patrice C. Roy, Muhammad S. Shah, Jin Yu y Sanjun Yan. "A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods". Journal of Healthcare Informatics Research 2, n.º 4 (20 de junio de 2018): 370–401. http://dx.doi.org/10.1007/s41666-018-0028-7.

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Omar, Yasser, Mohamed Abd-ElSalam ElSheikh y Rania Hodhod. "GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning". Health Informatics Journal 27, n.º 2 (abril de 2021): 146045822110092. http://dx.doi.org/10.1177/14604582211009276.

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Glaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening.

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An, Guangzhou, Kazuko Omodaka, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Toru Nakazawa, Hideo Yokota y Masahiro Akiba. "Comparison of Machine-Learning Classification Models for Glaucoma Management". Journal of Healthcare Engineering 2018 (19 de junio de 2018): 1–8. http://dx.doi.org/10.1155/2018/6874765.

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This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients’ background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.

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Diwakaran y S. Sheeba Jeya Sophia. "Survey on Automatic Detection of Glaucoma through Deep Learning Using Retinal Fundus Images". Journal of Biomedical Engineering and Medical Imaging 7, n.º 4 (1 de agosto de 2020): 11–15. http://dx.doi.org/10.14738/jbemi.74.8055.

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Glaucoma - a disease which causes damage to our eye's optic nerve and subsequently blinds the vision. This occurs due to increased intraocular pressure (IOP) which causes the damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. Presently, many works have been done towards automatic glaucoma detection using Fundus Images (FI) by extracting structural features. Structural features can be extracted from optic nerve head (ONH) analysis, cup to disc ratio (CDR) and Inferior, Superior, Nasal, Temporal (ISNT) rule in Fundus Image for glaucoma assessment.This survey presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under non-invasive methods of detection. Here, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. The objective of this survey is to obtain a technique which automatically analyze the retinal images of the eye with high efficiency and accuracy

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Sreng, Syna, Noppadol Maneerat, Kazuhiko Hamamoto y Khin Yadanar Win. "Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images". Applied Sciences 10, n.º 14 (17 de julio de 2020): 4916. http://dx.doi.org/10.3390/app10144916.

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Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.

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SHARMA, RAHUL, PRADIP SIRCAR, R. B. PACHORI, SULATHA V. BHANDARY y U. RAJENDRA ACHARYA. "AUTOMATED GLAUCOMA DETECTION USING CENTER SLICE OF HIGHER ORDER STATISTICS". Journal of Mechanics in Medicine and Biology 19, n.º 01 (febrero de 2019): 1940011. http://dx.doi.org/10.1142/s0219519419400116.

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Glaucoma is one of the leading causes of blindness. The raised intraocular pressure is one of the important modifiable risk factor causing glaucomatous optic nerve damage. Glaucomatous optic nerve damage is seen as increase in the cupping of the optic disc and loss of neuroretinal rim. An automated detection system using nonlinear higher order statistics (HOS) based method is used to capture the detailed information present in the fundus image efficiently. The center slice of bispectrum and bicepstrum are applied on fundus images. Various features are extracted from the diagonal of these central slices. In order to reduce the number of features the locality sensitive discriminant analysis (LSDA) data reduction technique method is implemented. The ranked LSDA features are fed to support vector machine (SVM) classifier with various kernels for automated glaucoma detection. The simulation is performed on two databases. The proposed algorithm has yielded classification accuracy of 98.8% and 95% using entire private and public databases, respectively. The proposed technique achieved the highest classification accuracy, hence, confirm the diagnosis of ophthalmologists and can be employed in the community health care centers and hospitals.

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Xiao, Zhang, Geng, Zhang, Wu y Liu. "Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning". Symmetry 11, n.º 7 (17 de julio de 2019): 933. http://dx.doi.org/10.3390/sym11070933.

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The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to other structures in the eye. This paper proposes a new network architecture we call Segmentation-ResNet Seg-ResNet that takes a residual network structure as the main body, introduces a channel weighting structure that automatically adjusts the dependence of the feature channels, re-calibrates the feature channels, and introduces a set of low-level features that are combined with high-level features to improve network performance. Pre-fusion features and fused features are symmetrical. Hence, this work correlates with the concept of symmetry. Combined with the training strategy of migration learning, the segmentation accuracy is improved while speeding up network convergence. The robustness and effectiveness of the proposed method are demonstrated by testing data from the GlaucomaRepo and Drishti-GS fundus image databases.

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Tesis sobre el tema "Glaucoma Optic nerve Machine learning"

Twa, Michael Duane. "Structural classification of glaucomatous optic neuropathy". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155267844.

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Miri, Mohammad Saleh. "A multimodal machine-learning graph-based approach for segmenting glaucomatous optic nerve head structures from SD-OCT volumes and fundus photographs". Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/5574.

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Glaucoma is the second leading cause of blindness worldwide. The clinical standard for monitoring the functional deficits in the retina that are caused by glaucoma is the visual field test. In addition to monitoring the functional loss, evaluating the disease-related structural changes in the human retina also helps with diagnosis and management of this progressive disease. The characteristic changes of retinal structures such as the optic nerve head (ONH) are monitored utilizing imaging modalities such as color (stereo) fundus photography and, more recently, spectral-domain optical coherence tomography (SD-OCT). With the inherent subjectivity and time required for manually segmenting retinal structures, there has been a great interest in automated approaches. Since both fundus and SD-OCT images are often acquired for the assessment of glaucoma, the automated segmentation approaches can benefit from combining the multimodal complementary information from both sources. The goal of the current work is to automatically segment the retinal structures and extract the proper parameters of the optic nerve head related to the diagnosis and management of glaucoma. The structural parameters include the cup-to-disc ratio (CDR) which is a 2D parameter and is obtainable from both fundus and SD-OCT modalities. Bruch's membrane opening-minimum rim width (BMO-MRW) is a recent 3D structural parameter that is obtainable from the SD-OCT modality only. We propose to use the complementary information from both fundus and SD-OCT modalities in order to enhance the segmentation of structures of interest. In order to enable combining information from different modalities, a feature-based registration method is proposed for aligning the fundus and OCT images. In addition, our goal is to incorporate the machine-learning techniques into the graph-theoretic approach that is used for segmenting the structures of interest. Thus, the major contributions of this work include: 1) use of complementary information from SD-OCT and fundus images for segmenting the optic disc and cup boundaries in both modalities, 2) identifying the extent that accounting for the presence of externally oblique border tissue and retinal vessels in rim-width-based parameters affects structure-structure correlations, 3) designing a feature-based registration approach for registering multimodal images of the retina, and 4) developing a multimodal graph-based approach to segment the optic nerve head (ONH) structures such as Internal Limiting Membrane (ILM) surface and Bruch's membrane surface's opening.

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Capítulos de libros sobre el tema "Glaucoma Optic nerve Machine learning"

Antony Ammal, M., D. Gladis y Atheek Shaik. "Metric Measures of Optic Nerve Head in Screening Glaucoma with Machine Learning". En Advances in Intelligent Systems and Computing, 583–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4389-4_54.

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Muramatsu, Chisako. "Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis". En Advances in Experimental Medicine and Biology, 121–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33128-3_8.

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Luo, Jiamin, Alex Noel Joseph Raj, Nersisson Ruban y Vijayalakshmi G. V. Mahesh. "Segmentation of Optic Disc From Fundus Image Based on Morphology and SVM Classifier". En Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, 116–44. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6690-9.ch007.

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Color fundus image is the most basic way to diagnose diabetic retinopathy, papillary edema, and glaucoma. In particular, since observing the morphological changes of the optic disc is conducive to the diagnosis of related diseases, accurate and effective positioning and segmentation of the optic disc is an important process. Optic disc segmentation algorithms are mainly based on template matching, deformable model and learning. According to the character that the shape of the optic disc is approximately circular, this proposed research work uses Kirsch operator to get the edge of the green channel fundus image through morphological operation, and then detects the optic disc by HOUGH circle transformation. In addition, supervised learning in machine learning is also applied in this chapter. First, the vascular mask is obtained by morphological operation for vascular erasure, and then the SVM classifier is segmented by HU moment invariant feature and gray level feature. The test results on the DRIONS fundus image database with expert-labeled optic disc contour show that the two methods have good results and high accuracy in optic disc segmentation. Even though seven different assessment parameters (sensitivity [Se], specificity [Sp], accuracy [Acc], positive predicted value [Ppv], and negative predicted value [Npv]) are used for performance assessment of the algorithm. Accuracy is considered as the criterion of judgment in this chapter. The average accuracy achieved for the nine random test set is 97.7%, which is better than any other classifiers used for segmenting Optical Disc from Fundus Images.

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Actas de conferencias sobre el tema "Glaucoma Optic nerve Machine learning"

Manassakorn, Anita, Kitiwat Khamwan, Dhammathat Owasirikul, Rath Itthipanichpong, Vera Sa-Ing y Supatana Auethavekiat. "Retinal Nerve Fiber Layer Defect Detection using Machine Learning on Optic Disc Photograph". En 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2021. http://dx.doi.org/10.1109/bhi50953.2021.9508567.

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Yu, Abidi y Artes. "A hybrid feature selection strategy for image defining features: towards interpretation of optic nerve images". En Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527847.

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