Disc Localization Research Articles

Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis

ObjectiveTo understand the current status, research hotspots, and trends of automatic segmentation of fundus lesion images worldwide, providing a reference for subsequent related studies. MethodsThe electronic database Web of Science Core Collection was searched for research in the field of automatic segmentation of fundus lesion images from 2007 to 2023. Visualization maps of countries, authors, institutions, journals, references, and keywords were generated and analyzed using the CiteSpace and VOSviewer software. ResultsAfter deduplication, 707 publications were sorted out, showing an overall increasing trend in publication volume. The countries with the highest publication counts were China, followed by India, the USA, the UK, Spain, Pakistan, and Singapore. A high degree of collaboration was observed among authors, and they cooperated widely. The keywords included "diabetic retinopathy," "deep learning," "vessel segmentation," "retinal images," "optic disc localization," and so forth, with keyword bursts starting in 2018 for "retinal images," "machine learning," "biomedical imaging," "deep learning," "convolutional neural networks," and "transfer learning." The most prolific author was U Rajendra Acharya from the University of Southern Queensland, and the journal with the most publications was Computer Methods and Programs in Biomedicine. ConclusionsCompared with manual segmentation of fundus lesion images, the use of deep learning models for segmentation is more efficient and accurate, which is crucial for patients with eye diseases. Although the number of related publications globally is relatively small, a growing trend is still witnessed, with broad connections between countries and authors, mainly concentrated in East Asia and Europe. Research institutions in this field are limited, and hence, the research on diabetic retinopathy and retinal vessel segmentation should be strengthened to promote the development of this area.

Accurate Preoperative Localization of Thoracolumbar Disc Extrusion in Dogs: A Prospective Controlled Study.

Intervertebral disc disease (IVDD) is a very common cause of spinal cord compression in dogs. The errors of human surgeons operating on the wrong level or wrong side is a recognized problem and has been largely analyzed. To date, little information is available regarding these errors in dogs. The objective of this study was to assess the accuracy of disc localization prior to possible surgery for IVDD to ensure the surgeon begins their incision directly over the extruded disc. Eighty-five chondrodystrophic or brachycephalic dogs that presented with IVDD confirmed by CT or MRI scan were included in the study. Two small Backhaus clamps were placed cranial and caudal to the lesion site and a control clamp placed at the wing of the ilium. The main interest was whether the surgeon was able to preoperatively pinpoint the correct site for the surgery. Dorsoventral radiographs were taken to verify by another person if the clamps had been placed in the correct position. If the result was incorrect, the surgeon was asked to try again without knowing that the clamps were incorrect, and another radiograph was taken. This was repeated until the position was correct. The results were recorded as correct or incorrect and the number of attempts were registered. The results suggested no significant trend over time for any of the outcomes examined. There were some slight improvements over time, but none of the results was close to statistical significance. The findings of the study showed that in the thoracolumbar region the surgeon has a higher chance of incorrectly marking the exact site for surgery.

A Comprehensive Approach for an Interpretable Diabetic Macular Edema Grading System Based on ConvUNext

Diabetic macular edema (DME) is a leading cause of vision impairment in diabetic patients, necessitating a timely and accurate diagnosis. This paper proposes a comprehensive system for DME grading using retinal fundus images. Our approach integrates multiple deep learning modules, each designed to address key aspects of the diagnostic process. The first module employs the ConvUNeXt model for segmenting hard exudates (HaEx), crucial indicators of DME. The second module uses RetinaNet for precise optic disc (OD) localization, which is essential for subsequent macula localization. The third module refines macula localization, leveraging preprocessing techniques to enhance image clarity. Finally, our system consolidates these results to provide interpretable DME grading. Experimental evaluations were conducted on the Messidor dataset, demonstrating the system’s robust performance. The HaEx segmentation module achieved a mean IoU of 70.5% and a Dice coefficient of 0.64. The OD localization module showed perfect accuracy, recall, and precision at 1.0. For macula localization, our method satisfied the 1R criterion with 99.38% accuracy. The DME grading module achieved an overall accuracy of 91.12%, with an AUC of 0.9334. Our method offers a balanced performance across accuracy, sensitivity, and specificity compared to other non-interpretable and partially interpretable methods.

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

AbstractDeep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations; hence, computer‐aided design (CAD) approach is preferred using images. In the context of image processing, convolution neural networks (CNNs) are preferred for classification because of their ability to grasp highly discriminate features from raw pixel intensities. In our approach, diagnosis of glaucoma is implemented by extracting the region of interest (ROI) by splitting the coefficients into recurrence decays and will improve the possibility of identifying even poorly differentiated exudates and upgrading the normal recurrence ranges. Later, a sequential deep neural network (DNN) model with a rectified linear unit (ReLU) and sigmoid function is designed to train the data with effective features matching from training and testing samples. The proposed model is implemented on two publicly available datasets (Drishti‐GS1 and ACRIMA) using 10‐fold cross validation (CV), 60:40 and 70:30 split ratio approaches, and performance is assessed using the metrics and plotted the region of convergence curves. The model is also tested on two more datasets (ORIGA and Refuge) to validate the robustness of the proposed model. The obtained simulation results and the evaluated performance metrics prove that our proposed model diagnose glaucoma from retinal fundus images effectively compared with other existing models.

An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting

Aims/Purpose: To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non‐experts.Methods: The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths.Results: QUARTZ's performance was evaluated on a subset of 3682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of −0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets.Conclusions: QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

OD-DeepNet: Semantic Classification by Deep Learning for Optic Disc Localization

OD-DeepNet: Semantic Classification by Deep Learning for Optic Disc Localization

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.

To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

Inhibition of Pyk2 Improves Cx43 Intercalated Disc Localization, Infarct Size, and Cardiac Function in Rats With Heart Failure.

Heart failure causes changes in Cx43 (Connexin43) regulation that are associated with arrhythmic heart disease. Pyk2 (proline-rich tyrosine kinase 2) is activated in cardiomyopathies and phosphorylates Cx43 to decrease intercellular communication. This study was designed to determine if Pyk2 inhibition improves cardiac function in a myocardial infarction (MI)-induced heart failure model in rats. MI (ligation of left anterior descending artery) rats were treated with the Pyk2 inhibitor PF4618433. Hemodynamic and structural parameters were monitored in Sham (n=5), MI-vehicle (n=5), and MI-PF4618433 (n=8) groups. Heart tissues were collected after 6 weeks to assess Pyk2 and Cx43 protein level and localization. PF4618433 produced no observed adverse effects and inhibited ventricular Pyk2. PF4618433 reduced the MI infarct size from 34% to 17% (P=0.007). PF4618433 improved stroke volume (P=0.031) and cardiac output (P=0.009) in comparison to MI-vehicle with values similar to the Sham group. PF4618433 also led to an increase in the ejection fraction (P=0.002) and fractional shortening (P=0.006) when compared with the MI-vehicle (32% and 35% improvement, respectively) yet were lower in comparison with the Sham group. Pyk2 inhibition decreased Cx43 tyrosine phosphorylation (P=0.043) and maintained Cx43 at the intercalated disc in the distal ventricle 6 weeks post-MI. Unlike other attempts to decrease Cx43 remodeling after MI-induced heart failure, inhibition of Pyk2 activity maintained Cx43 at the intercalated disc. This may have aided in the reduced infarct size (acute time frame) and improved cardiac function (chronic time frame). Additionally, we provide evidence that Pyk2 is activated following MI in human left ventricle, implicating a novel potential target for therapy in patients with heart failure.

Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding

Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database.

Optic Disc Localization in Normal and Pathological Retinal Images Using Dictionary-Based Approach

Optic Disc Localization in Normal and Pathological Retinal Images Using Dictionary-Based Approach

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

<p>Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.</p>

Optic Disc Localization using Fuzzy C Mean and DB Scan Clustering - A Comparative Analysis

In the diagnosis and early detection of Glaucoma and diabetic retinopathy, when delicate vasculature grows in the retina, precise identification and localization of the border optic disc are highly significant. This research provides an automated method to localize and detect the optic disc. The proposed method uses the clustering methodology to locate the optic disc region. Fuzzy C Mean and Density Based Scan (DB Scan) clustering approach is evaluated on the publicly accessible DRIVE, diaretdb1, diaretdb0, and databases, which were created to aid comparative investigations on optic disc localization and detection in retinal images. With Diaretdb0 and Drive DB, the DB Scan clustering approach obtained an accuracy of 94.11% and 81.18%, respectively, which is better than the Fuzzy C mean, and it performs DB scan better for the DiaretDB1 dataset.

Evaluation of the systemic inflammatory response in patients with a spinal synovial cyst or lumbar disc herniation

Aims: This study aimed to determine whether a systemic inflammatory response occurs in patients with spinal synovial cysts (SSC) and patients with lumbar disc herniation. Methods: Patients who underwent surgery for SSC or lumbar disc herniation between 2016-2022 were included in the study. To compare the results of these patients, patients who applied to the outpatient clinic due to headaches but did not find any abnormal findings in the radiological examinations were also included. Age, gender, duration of stay in the hospital, comorbidity, and histopathological evaluation results were recorded for all patients. The lumbar T2 weighted MR sagittal and axial images obtained on admission to the hospital were examined. The venous blood samples taken from the patients on first admission to the hospital were analyzed biochemically. Results: There was no statistical difference between the three groups regarding age, gender, and blood biochemistry parameters. Furthermore, no statistical difference was found between SSC patients and patients with lumbar disc herniation in age, gender, cyst or herniated disc localization levels, comorbidity, and blood biochemistry analysis results. Correlation analysis for the findings of all patients revealed that no parameters were correlated with the study groups. ROC-Curve test and Logistic Regression tests showed that no parameter could be a predictive marker in differentiating SSC from disc herniation. Conclusion: Study results demonstrated that neither SSC nor disc herniation caused a systemic inflammatory or allergic reaction in the patients. In addition, it was determined that no biochemical parameter could distinguish disc herniation from SSC.

Exudate identification in retinal fundus images using precise textural verifications

One of the most salient diseases of retina is Diabetic Retinopathy (DR) which may lead to irreparable damages to eye vision in the advanced phases. A large number of the people infected with diabetes experience DR. The early identification of DR signs facilitates the treatment process and prevents from blindness. Hard Exudates (HE) are bright lesions appeared in retinal fundus images of DR patients. Thus, the detection of HEs is an important task preventing the progress of DR. However, the detection of HEs is a challenging process due to their different appearance features. In this paper, an automatic method for the identification of HEs with various sizes and shapes is proposed. The method works based on a pixel-wise approach. It considers several semi-circular regions around each pixel. For each semi-circular region, the intensity changes around several directions and non-necessarily equal radiuses are computed. All pixels for which several semi-circular regions include considerable intensity changes are considered as the pixels located in HEs. In order to reduce false positives, an optic disc localization method is proposed in the post-processing phase. The performance of the proposed method has been evaluated on DIARETDB0 and DIARETDB1 datasets. The experimental results confirm the improved performance of the suggested method in term of accuracy.

Optic disc localization using graph traversal algorithm along blood vessel in polar retinal image

The optic disc (OD) is an important landmark of the retina and its location is essential for computer-aided diagnosis of diabetic retinopathy and glaucoma. This paper presented a new method that applies the proposed graph traversal algorithm for vertical vascular infrastructure to detect root nodes representing the location of optical discs. The proposed algorithms were designed to process the terrific view of blood vessels from a polar image without depending on the prominent OD characteristic of circular/elliptical shape and brightness. Before constructing the vascular infrastructure, the new multi-scale and multi-orientation top-hat filters were proposed to properly approximate blood vessels of different sizes and directions. The proposed method provided efficiency and certainty for complex retinal images such as low contrast OD with low illumination and distorted OD shape due to complex vascular structure. The algorithms evaluation was performed on five public fundus image databases for retinal image analysis. Experimental results revealed that the proposed algorithm identified valid optical discs with high accuracy as 91.21%, 95.09%, 88.45%, 91.66%, and 95.01% for the five corresponding data sets and outperformed comparison methods.

Vascular anatomy-based localization of intervertebral discs assisting needle puncture for constructing a mouse model of mechanical injury-induced lumbar intervertebral disc degeneration

Intervertebral disc degeneration (IDD) may be the primary cause of low back pain. Potential therapeutics for IDD must be validated in animal models, and their effectiveness quantified using functional metrics. Needle puncture of intervertebral discs (IVDs) has been used to induce IDD in mice and rats. Due to operational challenges, most animal IDD models are constructed using needle puncture of the caudal IVDs in mice, or by using larger animals, such as rats and rabbits. However, mouse IDD models involving lumbar IVD puncture are preferable because mice are genetically similar to humans and are the most commonly used transgenic animals, and because human IDD commonly affects the lumbar spine. We constructed a needle puncture-based mouse IDD model that relies on vascular anatomy to pinpoint lumbar IVDs. We evaluated the morphological and molecular changes in this model by using radiological, pathological, and immunostaining examinations. In our mechanical injury-induced IDD model, lumbar IVDs were accurately localized by injecting colored perfusates into the common iliac artery and vein, and right iliolumbar vein, which helped to visualize puncture positions, avoid neuromuscular injury, shorten the operation time, and decrease bleeding. Nucleus pulposus cells, defined by Krt19, and the disc height index gradually decreased after the surgery, and the degenerative effects peaked at 1 week. In conclusion, we established a mouse IDD model by performing precise puncture of lumbar IVDs via the ventral anterior approach assisted by vessel position. Our model effectively simulated the effects of IDD, and may serve as an efficient research tool.

Optic disc localization in fundus images through accumulated directional and radial blur analysis

Optic disc localization, a key preprocessing step in the analysis of color fundus images for diagnoses of eye diseases and the localization of various anatomical structures, is particularly challenging when input retina images contain abnormalities. In such cases, the disc can be confused with other anatomical structures such as fovea, exudates, vessel tree extraction, and retinopathy-related lesions. Herein, we present a method for effective optic disc detection and localization based on color and blur analysis. In this method, the input color fundus image is converted to CIE L*a*b* color space to enhance optic disc appearance and contrast, and the accumulated directional blur and extended-maxima transform are then applied to precisely extract optic disc candidates. Subsequently, radial blur is applied to each candidate to obtain better profiles and thus distinguish the optic disc from other candidates. Finally, the full width at 80% maximum (FW80M) metric is used to select the optic disc. The performance of the proposed method is evaluated using well-studied data sets, and comparison of the obtained results with those of state-of-the-art techniques reveals the effectiveness of our method and shows that it can precisely locate the disc position not only in normal cases but also in the presence of exudates and abnormalities.

A Study of Techniques for Segmenting the Spinal Cord

The spinal cord is one of the most important organs that controls communication between the brain and various parts of the body. It is highly susceptible to harmful infections and many diseases. An important criterion of clinical management is the appropriate localization and division of the spinal cord. Segmentation poses risks associated with variation in human anatomy and contrast variation in Magnetic Resonance Imaging. Therefore, an effective segmentation technique should be developed for effective division of the spinal cord and disc localization. In comparison, the survey contained here in provides an overview of different segmentation schemes for spinal cord segmentation. Now, there is an urgent need to develop an effective segmentation approach that is better than the current one segmentation methods. In this research article, a detailed survey of the various research activities presented by specific segmentation schemes based on the semi-automated, active contour model, Introduced partitioning, deformable model, probabilistic model and graph based partitioning. In addition, in-depth analysis and discussion are provided in accordance with the year of publication, evaluation dimensions, segmentation scheme, and magnetic Resonance Image Datasets, Dice Equivalence Coefficient (DSC), Accuracy.

ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images

Glaucoma is one of the prevalent causes of blindness in the modern world. It is a salient chronic eye disease that leads to irreversible vision loss. The impediments of glaucoma can be restricted if it is identified at primary stages. In this paper, a novel two-phase Optic Disk localization and Glaucoma Diagnosis Network (ODGNet) has been proposed. In the first phase, a visual saliency map incorporated with shallow CNN is used for effective OD localization from the fundus images. In the second phase, the transfer learning-based pre-trained models are used for glaucoma diagnosis. The transfer learning-based models such as AlexNet, ResNet, and VGGNet incorporated with saliency maps are evaluated on five public retinal datasets (ORIGA, HRF, DRIONS-DB, DR-HAGIS, and RIM-ONE) to differentiate between normal and glaucomatous images. This study’s experimental results demonstrate that the proposed ODGNet evaluated on ORIGA for glaucoma diagnosis is the most predictive model and achieve 95.75, 94.90, 94.75, and 97.85% of accuracy, specificity, sensitivity, and area under the curve, respectively. These results indicate that the proposed OD localization method based on the saliency map and shallow CNN is robust, accurate and saves the computational cost.