Automated detection of diabetic macular edema involving cystoids and serous retinal detachment

Maurya, Prashant Kumar; Gupta, Vishal; Singh, Maninder; Singh, Anushikha; Kumar, Basant; Mohan, Anand

doi:10.1016/j.optlastec.2020.106157

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Many experts and scholars have conducted related research on the segmentation of retina and lesions in OCT B-scan images. Maurya et al 8 proposed a method for detecting edema and identifying different types of DME, which segmented the cyst area by detecting and processing the pixel value of the region of interest. Wu…”

Section: Related Workmentioning

confidence: 99%

“…Many experts and scholars have conducted related research on the segmentation of retina and lesions in OCT B‐scan images. Maurya et al 8 proposed a method for detecting edema and identifying different types of DME, which segmented the cyst area by detecting and processing the pixel value of the region of interest. Wu et al 9 proposed a method for automatically detecting diabetic retinopathy based on Gaussian Mixture Model clustering and the level set and obtained the boundary of the retinal layer using an improved level set method.…”

Section: Related Workmentioning

confidence: 99%

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Automated segmentation of diabetic macular edema in OCT B‐scan images based on RCU‐Net

Zhang

Xiao

et al. 2022

Int J Imaging Syst Tech

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Diabetic macular edema (DME) is a typical fundus disease that can cause blindness in severe cases. The morphology of the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) layer in the retina and the macular edema (ME) area are important features for the diagnosis of DME. Doctors usually use non-invasive and high-resolution optical coherence tomography (OCT) to examine the fundus of the patient. However, manual diagnosis has low efficiency and strong subjectivity. Realizing the automatic segmentation of the ILM-RPE layer and ME is extremely important for the early diagnosis of DME. In this paper, the attention mechanism based on residual convolution module U-Net (RCU-Net) is proposed for the automatic segmentation of the retinal layer and cystoid edema lesions. Through the fusion of the residual structure and CBAM for feature extraction, the useful features in the channel and space are effectively strengthened, and the network can better learn different levels of information. The proposed network is combined with the Lov asz-softmax loss, which can better target the correlation between targets to obtain the optimal segmentation model during training. Finally, this paper compares the proposed method with several other segmentation methods. The experimental results show that the MIoU of the method in this model reaches 88.595%, and the Accuracy reaches 99.171%. The RCU-Net proposed in this paper is used to segment the ILM-RPE layer and ME region in the retina OCT B-scan images, and its overall performance is better than other networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated segmentation of diabetic macular edema in OCT B‐scan images based on RCU‐Net

Zhang

Xiao

et al. 2022

Int J Imaging Syst Tech

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“…Maurya et al [15] e automated method used to detect cystoid macula edema and serous retinal detachment in OCT image using gradient information-based segmentation of the retinal boundaries. e study detected only three types of DME, and results have to be compared using color fundus images for improvement inaccuracy.…”

Section: Techniques Used Limitationmentioning

confidence: 99%

Automatic Detection of Hard Exudates Shadow Region within Retinal Layers of OCT Images

Singh

Gupta

Singh

et al. 2022

Mathematical Problems in Engineering

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The optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. This paper presents a novel method for detecting hard exudates from retinal OCT images, often associated with macular edema near or within the outer plexiform layer. In this paper, an algorithm can automatically detect the presence of hard exudates in retinal OCT images, and these exudates appear as highly reflective spots. Still, they do not appear as noticeable bright spots because of their minute sizes in predevelopment phases. In the proposed work, we are using a method to detect the presence of hard exudates by analyzing their shadowing effect instead of focusing on brightness spots. The raster scanning operation is performed by traversing the retina horizontally, and noting up any change in normalized summation of brightness intensity (summing up the intensity from top to bottom retinal layers and normalized concerning retinal width) leads to the detection of minute as well as the presence for the detection of large exudates detection by differentiating this brightness intensity graph. The shadow region helps identify the hard exudates; in our proposed method, the output for three input images has been shown. There is an excellent agreement between the results generated by the proposed algorithm and the diagnostic opinion made by the ophthalmologist. The proposed method automatically detects the hard exudates using shadow regions, and it does not need any parameter settings or manual intervention. It can yield significant results by giving the position of shadow regions, which indicates the presence of exudates.

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“…For example, Chiu et al 9 adopted a segmentation framework based on graph theory and dynamic programming and segmented eight retinal layer boundaries; then a random forest retinal OCT image layering algorithm based on principal component analysis was proposed, which used principal component analysis to resample the features collected by random forest. 10 Maurya et al 11 proposed a method to detect and identify different types of edema areas. The cyst regions were segmented by detecting the region of interest and processing the pixel values in the restricted search region between retinal layers.…”

Section: Introductionmentioning

confidence: 99%

Joint segmentation of retinal layers and macular edema in optical coherence tomography scans based on RLMENet

Liu

Xiao

et al. 2022

Medical Physics

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Purpose The segmentation of retinal layers and fluid lesions on retinal optical coherence tomography (OCT) images is an important component of screening and diagnosing retinopathy in clinical ophthalmic treatment. We designed a novel network for accurate segmentation of the seven tissue layers of the retina and lesion areas of diabetic macular edema (DME), which can assist doctors to quantitatively analyze the disease. Methods In this article, we propose the Retinal Layer Macular Edema Network (RLMENet) model to achieve end‐to‐end joint segmentation of retinal layers and fluids. The network employs dense multi‐scale attention to enhance the extraction of retinal layer and fluid detail information and achieve efficient long‐range modeling, which improves the receptive field and obtains multi‐scale features. As the more complex decoder part is designed, which integrates more low‐level feature information on the decoder side, more features are extracted to gradually restore the resolution of the feature map and improve the segmentation accuracy. Results We used part of the OCT2017 dataset to train and verify the model to divide the data into a training set, validation set, and test set and set it to a 7:2:1 ratio. We evaluated our method on the ISIC2017 dataset. Experimental results showed that the RLMENet model designed in this work can accurately segment seven retinal tissue layers and DME lesions on the retinal OCT dataset. Finally, the MIoU value in the test set reached 86.55%. The model can be extended to other medical image segmentation datasets to achieve better segmentation performance. Conclusions The proposed method was superior to the existing segmentation methods, achieved a more refined segmentation effect, and provided an auxiliary analysis tool for clinical diagnosis and treatment.

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Automated detection of diabetic macular edema involving cystoids and serous retinal detachment

Cited by 8 publications

References 14 publications

Automated segmentation of diabetic macular edema in OCT B‐scan images based on RCU‐Net

Automated segmentation of diabetic macular edema in OCT B‐scan images based on RCU‐Net

Automatic Detection of Hard Exudates Shadow Region within Retinal Layers of OCT Images

Joint segmentation of retinal layers and macular edema in optical coherence tomography scans based on RLMENet

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