Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions. Thus, automatic segmentation of IRCs can be beneficial to investigate disease progression. Methods: In this study, automatic IRC segmentation is accomplished by building three different masks in three unsupervised segmentation levels of a hierarchical framework. In the first level, the ROI-mask (R-mask) is built, and the retina area is cropped based on this mask. In the second level, the prune-mask (P-mask) is built, and the searching space is significantly reduced toward the target objects using this mask; and finally in the third level, by applying the Markov random field (MRF) model and employing intensity and contextual information, the cyst mask (C-mask) is extracted. Results: The proposed method is evaluated on three datasets including OPTIMA, UMN, and KER-MANY datasets. The experimental results showed that the proposed method is effective with a mean dice coefficient rate of 0.74, 0.75 and 0.79 by the intersection of ground truths on the OPTIMA, UMN and KERMANY datasets, respectively. Conclusion: The proposed method outperforms the state-of-the-art methods on the OPTIMA and UMN datasets while achieving comparable results to the most recently proposed method on the KERMANY dataset.
Most of the retinal blood vessel segmentation approaches use low level features, resulting in segmenting non-vessel structures together with vessel structures in pathological retinal images. In this paper, a new segmentation method based on high level features is proposed which can process the structure of vessel and non-vessel independently. In this method, segmentation is done in two steps. First, using low level features segmentation is accomplished. Second, using high level features, the non-vessel components are removed. For evaluation, STARE database is used which is publicly available in this field. The results show that the proposed method has 0.9536 accuracy and 0.0191 false positive average on all images of the database and 0.9542 accuracy and 0.0236 false positive average on pathological images. Therefore, the proposed approach shows acceptable accuracy on all images compared to other state of the art methods, and the least false positive average on pathological images.
Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression. Therefore, automatic segmentation and quantification of abnormal CNP regions can be helpful to monitor the patient's treatment process. We propose an automatic method for segmentation of abnormal CNP regions on the superficial and deep capillary plexuses of optical coherence tomography angiography (OCTA) images using the marker-controlled watershed algorithm. The proposed method has three main steps. In the first step, original images are enhanced using the vesselness filter and then foreground and background marker images are computed. In the second step, abnormal CNP region candidates are segmented using the marker-controlled watershed algorithm, and in the third step, the candidates are modeled using an undirected weighted graph and finally, by applying merging and removing procedures correct abnormal CNP regions are identified. The proposed method was evaluated on a dataset with 36 normal and diabetic subjects using the ground truth obtained by two observers. The results show the proposed method outperformed some of the state-of-the-art methods on the superficial and deep capillary plexuses according to the most important metrics.
Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential step in automatic detection of DR. In this paper, a new MAs detection method is presented. The proposed approach consists of two main steps. In the first step, the MA candidates are detected based on local applying of Markov random field model (MRF). In the second step, these candidate regions are categorized to identify the correct MAs using 23 features based on shape, intensity and Gaussian distribution of MAs intensity. The proposed method is evaluated on DIARETDB1 which is a standard and publicly available database in this field. Evaluation of the proposed method on this database resulted in the average sensitivity of 0.82 for a confidence level of 75 as a ground truth. The results show that our method is able to detect the low contrast MAs with the background while its performance is still comparable to other state of the art approaches.
In this article, we propose a new U‐Net‐based approach for intraretinal cyst segmentation across different vendors that improve some of the challenges faced by previous deep‐based techniques. The proposed method has two main steps: (1) prior information embedding and input data adjustment, (2) the segmentation model. In the first step, we inject the information into the network in a way that overcomes some of the network limitations in receiving data and learning important contextual knowledge. And in the next step, we introduce a connection module between the encoder and decoder parts that transfers information more effectively from the encoder to the decoder. Two public datasets, namely, OPTIMA and KERMANY, are employed to evaluate the proposed method. The results show that the proposed method is an efficient vendor‐independent approach for the segmentation of intraretinal cystoid fluid with mean Dice values of 0.78 and 0.81 on the OPTIMA and KERMANY datasets, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.