OCT Segmentation via Deep Learning: A Review of Recent Work

Joshi, Neil; Liu, T. Y. Alvin; Bressler, Neil M.; DeBuc, Delia Cabrera; Burlina, P.

doi:10.1007/978-3-030-21074-8_27

Cited by 5 publications

(6 citation statements)

References 34 publications

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“…For retinal OCT B-scan images, segmentation algorithms are typically divided into two categories [6]: retinal layer boundary and retinal layer extraction. Most early algorithms focused on fitting the retinal layer boundary based on image intensity and its derivatives.…”

Section: Introductionmentioning

confidence: 99%

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography

Zhang

Nan

et al. 2023

Life

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Optical coherence tomography (OCT) provides unique advantages in ophthalmic examinations owing to its noncontact, high-resolution, and noninvasive features, which have evolved into one of the most crucial modalities for identifying and evaluating retinal abnormalities. Segmentation of laminar structures and lesion tissues in retinal OCT images can provide quantitative information on retinal morphology and reliable guidance for clinical diagnosis and treatment. Convolutional neural networks (CNNs) have achieved success in various medical image segmentation tasks. However, the receptive field of convolution has inherent locality constraints, resulting in limitations of mainstream frameworks based on CNNs, which is still evident in recognizing the morphological changes of retina OCT. In this study, we proposed an end-to-end network, TranSegNet, which incorporates a hybrid encoder that combines the advantages of a lightweight vision transformer (ViT) and the U-shaped network. The CNN features under multiscale resolution are extracted based on the improved U-net backbone, and a ViT with the multi-head convolutional attention is introduced to capture the feature information in a global view, realizing accurate localization and segmentation of retinal layers and lesion tissues. The experimental results illustrate that hybrid CNN-ViT is a strong encoder for retinal OCT image segmentation tasks and the lightweight design reduces its parameter size and computational complexity while maintaining its outstanding performance. By applying TranSegNet to healthy and diseased retinal OCT datasets separately, TranSegNet demonstrated superior efficiency, accuracy, and robustness in the segmentation results of retinal layers and accumulated fluid than the four advanced segmentation methods, such as FCN, SegNet, Unet and TransUnet.

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Section: Introductionmentioning

confidence: 99%

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography

Zhang

Nan

et al. 2023

Life

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“…For example, CNNs have been employed for segmentation of choroidal vessel ( Liu et al, 2019 ), capillary ( Mekonnen et al, 2021 ), retinal layer boundaries ( Shah et al, 2018 ) and skin ( Kepp et al, 2019 ). Despite CNNs show considerable promise in OCT image analysis, their application in OCT images is still in the early stages ( Pekala et al, 2019 ; Badar et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks

Bao

Zhou

Yang

et al. 2023

Front. Bioeng. Biotechnol.

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Organoids are advancing the development of accurate prediction of drug efficacy and toxicity in vitro. These advancements are attributed to the ability of organoids to recapitulate key structural and functional features of organs and parent tumor. Specifically, organoids are self-organized assembly with a multi-scale structure of 30–800 μm, which exacerbates the difficulty of non-destructive three-dimensional (3D) imaging, tracking and classification analysis for organoid clusters by traditional microscopy techniques. Here, we devise a 3D imaging, segmentation and analysis method based on Optical coherence tomography (OCT) technology and deep convolutional neural networks (CNNs) for printed organoid clusters (Organoid Printing and optical coherence tomography-based analysis, OPO). The results demonstrate that the organoid scale influences the segmentation effect of the neural network. The multi-scale information-guided optimized EGO-Net we designed achieves the best results, especially showing better recognition workout for the biologically significant organoid with diameter ≥50 μm than other neural networks. Moreover, OPO achieves to reconstruct the multiscale structure of organoid clusters within printed microbeads and calibrate the printing errors by segmenting the printed microbeads edges. Overall, the classification, tracking and quantitative analysis based on image reveal that the growth process of organoid undergoes morphological changes such as volume growth, cavity creation and fusion, and quantitative calculation of the volume demonstrates that the growth rate of organoid is associated with the initial scale. The new method we proposed enable the study of growth, structural evolution and heterogeneity for the organoid cluster, which is valuable for drug screening and tumor drug sensitivity detection based on organoids.

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“…Many machine learning techniques have been developed in ophthalmology, including applications for the identification of retinal landmarks, retinal pathology segmentation, and retinal disease classification. As a result, reviews of deep learning works have been actively published, with some covering specific domains and some covering ophthalmology in general [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. A review of the applications in sub-domains has the advantage of providing detailed and rich content, but it can be difficult to note the importance of the applications in a large context, and a review may contain information that is not meaningful from an engineer’s point of view.…”

Section: Introductionmentioning

confidence: 99%

Review of Machine Learning Applications Using Retinal Fundus Images

2022

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Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

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OCT Segmentation via Deep Learning: A Review of Recent Work

Cited by 5 publications

References 34 publications

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography

Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks

Review of Machine Learning Applications Using Retinal Fundus Images

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