Simultaneous Segmentation of Layers and Fluids in Retinal OCT Images

Ndipenoch, Nchongmaje; Miron, Alina; Wang, Zidong; Li, Yongmin

doi:10.1109/cisp-bmei56279.2022.9979957

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…In DeepResUNet++, 17 the system's complexity is higher, resulting in increased training time and reduced model speed due to a large number of trainable parameters and time‐consuming for real‐time applications.…”

Section: Resultsmentioning

confidence: 99%

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Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Jeya Prabha,

Sameera Fathimal,

Meghana

et al. 2023

Int J Imaging Syst Tech

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Optical coherence tomography is a non‐invasive imaging technique that provides micrometer‐resolution images of retinal structures. These images can assist in identifying changes under the retina's surface, such as edema. This study proposes a novel deep learning model AR U‐Net++ for segmenting retinal layers and fluids. The four retinal layers ILM (Internal Limiting Membrane), IPL (Inner Plexiform Layer), RPE (Retinal Pigment Epithelium), BM (Bruch Membrane), and IRF (Intra Retinal Fluid), SRF (Sub Retinal Fluid), and PED (Pigment Epithelial Detachment) are segmented using AR U‐Net++. The proposed architecture AR U‐Net++ achieves better accuracy (99.67%), mean IoU (0.84), and dice coefficient (0.94) than the existing models of U‐Net, AR U‐Net, and AR W‐Net. The novelty of the suggested model AR U‐Net++ is to identify the exact location and depth of the retinal fluid in between the retinal layers and generating reports that aids the clinicians in the diagnosis of Age related Macular Degeneration.

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Section: Resultsmentioning

confidence: 99%

“…Ndipenoch et al 17 introduced Deep_ResUNet++ for the purpose of segmenting retinal layers and retinal fluids evaluating the AROI dataset. The researchers employed an identical ResUNet++ 18 architecture, albeit with the inclusion of an additional five convolution blocks instead of the original four.…”

Section: Introductionmentioning

confidence: 99%

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Jeya Prabha,

Sameera Fathimal,

Meghana

et al. 2023

Int J Imaging Syst Tech

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“…The Deep-ResUNet++ is presented in [57] for simultaneous segmentation of layers and fluids in retinal OCT images. The approached incorporated residual connections, ASPP blocks and Squeeze and Exciting blocks into the traditional 2D UNet [62] architecture to simultaneously segment 3 retinal layers, 3 fluids and 2 background classes from 1136 B-Scans from 24 patients suffering from wet AMD.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data Sources

Ndipenoch,

Miron,

2024

IEEE Access

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Retinal Optical Coherence Tomography (OCT) is a non-invasive cross-sectional scan of the eye that provides qualitative 3D visualization of the retinal anatomy. It is used to study the retinal structure and the presence of pathogens. The advent of retinal OCT has transformed ophthalmology and is currently paramount for the diagnosis, monitoring, and treatment of many eye diseases, including macular edema, which impairs vision severely, and glaucoma, which can cause irreversible blindness. However, the quality of OCT images can vary among device manufacturers. Deep learning methods have been successful in the medical image segmentation community, but it is not yet clear if the level of success can be generalized across images collected from different device vendors. In this study, we provide a comprehensive review of current deep learning segmentation methods applied to OCT images. Furthermore, to investigate the problem of variant of data sources from OCT device vendors, we analyse a selection of the most representative methods to address this problem, including those on the top of the RETOUCH competition such as nnUNet and its variant nnUNet_RASPP, SAM and its variant SAMedOCT, IAUNet_SPP_CL, alongside other state-ofthe-art algorithms. The algorithms were validated on the RETOUCH challenge dataset, which was acquired from three device vendors across three medical centers from patients suffering from two retinal disease types. Experimental results show that for several tasks of segmentation, detection and generalisation performance from the retinal images, while fine-tuned large foundation models such as SAMedOCT have demonstrated promising performance, the specifically designed and trained models such as nnUNet and nnUNet_RASPP still offer a slight advantage overall. Also, the nnUNet_RASPP obtained the best performance of 82.3% of mean Dice score for fluid segmentation.

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“…Deep learning addresses a groundbreaking frontier in the field of data science, offering unparalleled flexibility contrasted with conventional artificial intelligence [7]. Deep learning models, inspired by the human brain and its complicated neural networks, have acquired noticeable quality, especially in handling image analysis [3,8,9]. They have shown promise in illness determination and treatment in the clinical field.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer

Abdullah,

Siddique,

Shaukat

et al. 2024

Diagnostics

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Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

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Simultaneous Segmentation of Layers and Fluids in Retinal OCT Images

Cited by 5 publications

References 21 publications

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data Sources

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer

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