Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks

García, Gabriel; Amor, Rocío del; Colomer, Adrián; Verdú‐Monedero, Rafael; Morales‐Sánchez, Juan; Naranjo, Valery

doi:10.1016/j.artmed.2021.102132

Cited by 23 publications

(7 citation statements)

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“…The new resulting model is called H-DLpNet (where H refers to human, DL to deep learning, p to peripapillary, and Net stands for neural network). This approach was widely applied in other OCT-based state-of-the-art works intended for glaucoma classification (see e.g., [ 44 ]). The fully convolutional network (FCN) architecture was composed of three encoder-decoder blocks.…”

Section: Resultsmentioning

confidence: 99%

“…Approaches based on machine learning were also used to automatically segment and analyze the retinal layers in OCT images. For example, a surrogate-assisted method based on the convolutional neural networks was proposed in [ 43 ] to classify retinal OCT images; in [ 44 ], tailored prototypical neural networks were developed for glaucoma grading using raw circumpapillary B-scans; the effective features of the boundaries were extracted in [ 45 ] with a convolutional neural network to obtain the final retinal boundaries using a graph-based search on the probability maps; in [ 46 ], the characteristics of retinal layers were learned with a deep neural network in a multiscale approach performing the segmentation with an encoder-decoder module; layers and fluid in 3-D OCT retinal images of subjects suffering from central serous retinopathy were simultaneously segmented in [ 47 ] with random forest classifiers; the most significant retinal layers in rodent OCT images were detected in [ 48 ] with a encoder-decoder fully convolutional network (FCN) architecture. Recently, in [ 49 ], the order of the layers was included explicitly in their deep learning method, since most of previous approaches did not consider it and this could lead to topological errors.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Berenguer-Vidal

Verdú‐Monedero

Morales‐Sánchez

et al. 2021

Sensors

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Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Berenguer-Vidal

Verdú‐Monedero

Morales‐Sánchez

et al. 2021

Sensors

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“…Fewer works have approached the problem of segmenting the layers of the retina in glaucoma patients, attending to the disease-specific degeneration that affects them. Even then, these are limited to a single scan pattern, namely the circumpapillary scan (28)(29)(30)(31) or the ONH (32), while foregoing the analysis of the other relevant views for the diagnosis of glaucoma. In this sense, to the best of our knowledge, the segmentation of the relevant retinal structures for the diagnosis of glaucoma in all of the OCT views used for its assessment remains to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

Gende¹,

Moura²,

Fernández‐Vigo³

et al. 2023

Quant Imaging Med Surg

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Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by this disease by assessing the retinal layers in different regions of the eye, using different optical coherence tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of the retina. These images are used to measure the thickness of the retinal layers in different regions. Methods:We present two approaches for the multi-region segmentation of the retinal layers in OCT images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a single module to segment all of the scan patterns, considering them as a single domain. The second approach uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable module to analyse each image. Results:The proposed approaches produced satisfactory results with the first approach achieving a dice coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results for the better represented circle and cube scan patterns. Conclusions:To the extent of our knowledge, this is the first proposal in the literature for the multi-view segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learningbased systems for aiding in the diagnosis of this relevant pathology.

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“…García et al (2021b) andCheng et al (2011) evaluated their proposed models on other glaucoma datasets with over 1000 samples. SmartDSP team (Maetschke et al 2019) evaluated their model on the MICCAI2021 Contest: GAMMA…”

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confidence: 99%

Mstnet: method for glaucoma grading based on multimodal feature fusion of spatial relations

Wang,

Zhang

et al. 2023

Phys. Med. Biol.

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Objective:The objective of this study is to develop an efficient multimodal learning framework for the classification of glaucoma. Glaucoma is a group of eye diseases that can result in vision loss and blindness, often due to delayed detection and treatment. Fundus images and optical coherence tomography (OCT) images have proven valuable for the diagnosis and management of glaucoma. However, current models that combine features from both modalities often lack efficient spatial relationship modeling. Approach:In this study, we propose an innovative approach to address the classification of glaucoma. We focus on leveraging the features of OCT volumes and harness the capabilities of transformer models to capture long-range spatial relationships. To achieve this, we introduce a 3D transformer model to extract features from OCT volumes, enhancing the model's effectiveness. Additionally, we employ downsampling techniques to enhance model efficiency. We then utilize the spatial feature relationships between OCT volumes and fundus images to fuse the features extracted from both sources. Main Results:Our proposed framework has yielded remarkable results, particularly in terms of glaucoma grading performance. We conducted our experiments using the GAMMA dataset, and our approach outperformed traditional feature fusion methods. By effectively modeling spatial relationships and combining OCT volume and fundus map features, our framework achieved outstanding classification results.Significance:This research is of significant importance in the field of glaucoma diagnosis and management. Efficient and accurate glaucoma classification is essential for timely intervention and prevention of vision loss. Our proposed approach, which integrates 3D transformer models, offers a novel way to extract and fuse features from OCT volumes and fundus images, ultimately enhancing the effectiveness of glaucoma classification. This work has the potential to contribute to improved patient care, particularly in the early detection and treatment of glaucoma, thereby reducing the risk of vision impairment and blindness.

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Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks

Cited by 23 publications

References 82 publications

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

Mstnet: method for glaucoma grading based on multimodal feature fusion of spatial relations

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