Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review

• URL: http://arxiv.org/abs/2109.02322v1
• Date: Mon, 6 Sep 2021 09:45:57 GMT
• Title: Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review
• Authors: Rita Marques, Danilo Andrade De Jesus, Jo\~ao Barbosa Breda, Jan Van Eijgen, Ingeborg Stalmans, Theo van Walsum and Stefan Klein and Pedro G. Vaz and Luisa S\'anchez Brea
• Abstract summary: The optic nerve head represents the intraocular section of the optic nerve (ONH) The advent of optical coherence tomography has enabled the evaluation of novel optic nerve head parameters. Deep learning-based algorithms provide the highest accuracy, sensitivity and specificity for segmenting the different structures of the ONH.
• Score: 4.777796444711511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The optic nerve head represents the intraocular section of the optic nerve (ONH), which is prone to damage by intraocular pressure. The advent of optical coherence tomography (OCT) has enabled the evaluation of novel optic nerve head parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane opening minimum-rim-width, these seem to be promising optic nerve head parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these optical coherence tomography derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of optic nerve head in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 27 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analyzed. The results show that deep learning-based algorithms provide the highest accuracy, sensitivity and specificity for segmenting the different structures of the ONH including the LC. However, a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches has been observed, highlighting the importance and need of standardized methodologies for ONH segmentation.

Related papers

• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography [48.7576911714538]
  We developed a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field OCT scans. Our classification algorithm was able to segment ONH and macular tissues with a DC of 0.94 $pm$ 0.003. This may encourage the mainstream adoption of 3D wide-field OCT scans.
  arXiv  Detail & Related papers  (2022-10-13T01:51:29Z)
• Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis [52.06403518904579]
  The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from 3D ONH point clouds. Our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.
  arXiv  Detail & Related papers  (2022-04-14T12:52:10Z)
• Deep Learning based Framework for Automatic Diagnosis of Glaucoma based on analysis of Focal Notching in the Optic Nerve Head [0.2580765958706854]
  We propose a deep learning-based pipeline for automatic segmentation of optic disc (OD) and optic cup (OC) regions from Digital Fundus Images (DFIs) This methodology has utilized focal notch analysis of neuroretinal rim along with cup-to-disc ratio values as classifying parameters to enhance the accuracy of Computer-aided design (CAD) systems in analyzing glaucoma. The proposed pipeline was evaluated on the freely available DRISHTI-GS dataset with a resultant accuracy of 93.33% for detecting Glaucoma from DFIs.
  arXiv  Detail & Related papers  (2021-12-10T18:58:40Z)
• Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks [1.1601676598120785]
  Glaucoma is one of the leading causes of blindness worldwide. We propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms.
  arXiv  Detail & Related papers  (2021-06-25T10:53:01Z)
• Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality Lost Terms [4.93836246080317]
  Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses. We propose a convolutional neural network model and optimize its performance using a new multi-term loss function. Our model segments two classes of major artifacts and detects the anatomical layers within the thickened vessel wall regions.
  arXiv  Detail & Related papers  (2021-05-11T15:52:31Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation [18.58056402884405]
  Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors. This paper proposes a convolutional neural network based weakly-supervised lesion segmentation method.
  arXiv  Detail & Related papers  (2020-01-23T15:15:53Z)
• Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning [2.571523045125397]
  The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. The monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes.
  arXiv  Detail & Related papers  (2020-01-23T12:55:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.