AROI: Annotated Retinal OCT Images Database

Melinščak, Martina; Radmilović, Marin; Vatavuk, Zoran; Lončarić, Sven

doi:10.23919/mipro52101.2021.9596934

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Melinščak 45 employed an attention‐based U‐Net model to perform the segmentation of retinal layers and retinal fluids using the AROI dataset. The achieved dice scores for PED, SRF, and IRF were 0.674, 0.600, and 0.563, respectively.…”

Section: Resultsmentioning

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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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%

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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“…In this study, the performance of the proposed model is meticulously assessed and benchmarked against CNNs lacking attention mechanisms using the publicly accessible AROI dataset [ 39 ]. This dataset comprises macular SD-OCT volumes recorded with the Zeiss Cirrus HD OCT 4000 device, featuring 128 B-scans with 1024 × 512 pixels per OCT volume resolution.…”

Section: Methodsmentioning

confidence: 99%

Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images

Karn,

Abdulla

2024

Bioengineering

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This paper presents a novel U-Net model incorporating a hybrid attention mechanism for automating the segmentation of sub-retinal layers in Optical Coherence Tomography (OCT) images. OCT is an ophthalmology tool that provides detailed insights into retinal structures. Manual segmentation of these layers is time-consuming and subjective, calling for automated solutions. Our proposed model combines edge and spatial attention mechanisms with the U-Net architecture to improve segmentation accuracy. By leveraging attention mechanisms, the U-Net focuses selectively on image features. Extensive evaluations using datasets demonstrate that our model outperforms existing approaches, making it a valuable tool for medical professionals. The study also highlights the model’s robustness through performance metrics such as an average Dice score of 94.99%, Adjusted Rand Index (ARI) of 97.00%, and Strength of Agreement (SOA) classifications like “Almost Perfect”, “Excellent”, and “Very Strong”. This advanced predictive model shows promise in expediting processes and enhancing the precision of ocular imaging in real-world applications.

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“…It can be observed that compared to successful examples of other datasets, the successful examples of endoscopic OCT are actually not perfect, while the failure cases are indeed unsatisfactory. [34] contains 1136 annotated B-scans and associated raw high-resolution images from 24 patients with age-related macular degeneration (AMD). An ophthalmologist annotated three retinal layers and three retinal fluids in each B-scan.…”

Section: Fundus Imagesmentioning

confidence: 99%

Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

et al. 2023

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Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics.

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AROI: Annotated Retinal OCT Images Database

Cited by 11 publications

References 11 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

Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images

Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

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