Vessel Segmentation In Fundus Images Research Articles

A multi-scale feature extraction and fusion-based model for retinal vessel segmentation in fundus images.

In response to the challenge of low accuracy in retinal vessel segmentation attributed to the minute nature of the vessels, this paper proposes a retinal vessel segmentation model based on an improved U-Net, which combines multi-scale feature extraction and fusion techniques. An improved dilated residual module was first used to replace the original convolutional layer of U-Net, and this module, coupled with a dual attention mechanism and diverse expansion rates, facilitates the extraction of multi-scale vascular features. Moreover, an adaptive feature fusion module was added at the skip connections of the model to improve vessel connectivity. To further optimize network training, a hybrid loss function is employed to mitigate the class imbalance between vessels and the background. Experimental results on the DRIVE dataset and CHASE_DB1 dataset show that the proposed model has an accuracy of 96.27% and 96.96%, sensitivity of 81.32% and 82.59%, and AUC of 98.34% and 98.70%, respectively, demonstrating superior segmentation performance.

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Vessel Segmentation in Fundus Images with Multi-Scale Feature Extraction and Disentangled Representation

Vessel segmentation in fundus images is crucial for diagnosing eye diseases. The rapid development of deep learning has greatly improved segmentation accuracy. However, the scale of the retinal blood-vessel structure varies greatly, and there is a lot of noise unrelated to blood-vessel segmentation in fundus images, which increases the complexity and difficulty of the segmentation algorithm. Comprehensive consideration of factors like scale variation and noise suppression is imperative to enhance segmentation accuracy and stability. Therefore, we propose a retinal vessel segmentation method based on multi-scale feature extraction and decoupled representation. Specifically, we design a multi-scale feature extraction module at the skip connections, utilizing dilated convolutions to capture multi-scale features and further emphasizing crucial information through channel attention modules. Additionally, to separate useful spatial information from redundant information and enhance segmentation performance, we introduce an image reconstruction branch to assist in the segmentation task. The specific approach involves using a disentangled representation method to decouple the image into content and style, utilizing the content part for segmentation tasks. We conducted experiments on the DRIVE, STARE, and CHASE_DB1 datasets, and the results showed that our method outperformed others, achieving the highest accuracy across all three datasets (DRIVE:0.9690, CHASE_DB1:0.9757, and STARE:0.9765).

Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.

Automated retinal blood vessel segmentation in fundus images provides important evidence to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive way. However, segmenting blood vessels in fundus images is a challenging task, due to the high variety in scale and appearance of blood vessels and the high similarity in visual features between the lesions and retinal vascular. Inspired by the way that the visual cortex adaptively responds to the type of stimulus, we propose a Stimulus-Guided Adaptive Transformer Network (SGAT-Net) for accurate retinal blood vessel segmentation. It entails a Stimulus-Guided Adaptive Module (SGA-Module) that can extract local-global compound features based on inductive bias and self-attention mechanism. Alongside a light-weight residual encoder (ResEncoder) structure capturing the relevant details of appearance, a Stimulus-Guided Adaptive Pooling Transformer (SGAP-Former) is introduced to reweight the maximum and average pooling to enrich the contextual embedding representation while suppressing the redundant information. Moreover, a Stimulus-Guided Adaptive Feature Fusion (SGAFF) module is designed to adaptively emphasize the local details and global context and fuse them in the latent space to adjust the receptive field (RF) based on the task. The evaluation is implemented on the largest fundus image dataset (FIVES) and three popular retinal image datasets (DRIVE, STARE, CHASEDB1). Experimental results show that the proposed method achieves a competitive performance over the other existing method, with a clear advantage in avoiding errors that commonly happen in areas with highly similar visual features. The sourcecode is publicly available at: https://github.com/Gins-07/SGAT.

Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

LUVS-Net: A Lightweight U-Net Vessel Segmentor for Retinal Vasculature Detection in Fundus Images

This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large neural networks. The computational challenges arise due to low-quality retinal images, wide variance in image acquisition conditions and disparities in intensity. Consequently, the training of existing segmentation methods requires a multitude of trainable parameters for the training of networks, resulting in computational complexity. The proposed Lightweight U-Net for Vessel Segmentation Network (LUVS-Net) can achieve high segmentation performance with only a few trainable parameters. This network uses an encoder–decoder framework in which edge data are transposed from the first layers of the encoder to the last layer of the decoder, massively improving the convergence latency. Additionally, LUVS-Net’s design allows for a dual-stream information flow both inside as well as outside of the encoder–decoder pair. The network width is enhanced using group convolutions, which allow the network to learn a larger number of low- and intermediate-level features. Spatial information loss is minimized using skip connections, and class imbalances are mitigated using dice loss for pixel-wise classification. The performance of the proposed network is evaluated on the publicly available retinal blood vessel datasets DRIVE, CHASE_DB1 and STARE. LUVS-Net proves to be quite competitive, outperforming alternative state-of-the-art segmentation methods and achieving comparable accuracy using trainable parameters that are reduced by two to three orders of magnitude compared with those of comparative state-of-the-art methods.

DA-Res2UNet: Explainable blood vessel segmentation from fundus images

Blood vessel segmentation in fundus images is necessary for the diagnosis of ophthalmic diseases. In recent years, deep learning has achieved eminent performance in blood vessel segmentation, and there still exist challenges to reduce misidentification and improve microvascular segmentation accuracy. One reason is that traditional Convolutional Neural Network (CNN) can not effectively extract multiscale information and discard the unnecessary information. Another reason is we can’t explain why some blood vessels fail to be identified. On the one hand, this paper proposes a Dual Attention Res2UNet (DA-Res2UNet) model. The DA-Res2UNet model uses Res2block rather than CNN to obtain more multiscale information and adds Dual Attention to help the model focus on important information and discard unnecessary information. On the other hand, the explainable method based on a pre-trained fundus image generator is adopted to explore how the model identifies blood vessels. We deduce several special situations that lead to the misidentification based on the model’s explanation and adjust the dataset for these special cases. The adjusted datasets significantly reduce the misidentification in the CHASE_DB1 dataset. Finally, the model trained by the adjusted datasets achieves the state-of-the-art F1-score of 81.88%, 82.77%, and 83.96% on the CHASE_DB1, DRIVE and STARE datasets, respectively.

MINet: Multi-scale input network for fundus microvascular segmentation

Vessel segmentation in fundus images is a key procedure in the diagnosis of ophthalmic diseases, which can play a role in assisting doctors in diagnosis. Although current deep learning-based methods can achieve high accuracy in segmenting fundus vessel images, the results are not satisfactory in segmenting microscopic vessels that are close to the background region. The reason for this problem is that thin blood vessels contain very little information, with the convolution operation of each layer in the deep network, this part of the information will be randomly lost. To improve the segmentation ability of the small blood vessel region, a multi-input network (MINet) was proposed to segment vascular regions more accurately. We designed a multi-input fusion module (MIF) in the encoder, which is proposed to acquire multi-scale features in the encoder stage while preserving the microvessel feature information. In addition, to further aggregate multi-scale information from adjacent regions, a multi-scale atrous spatial pyramid (MASP) module is proposed. This module can enhance the extraction of vascular information without reducing the resolution loss. In order to better recover segmentation results with details, we designed a refinement module, which acts on the last layer of the network output to refine the results of the last layer of the network to get more accurate segmentation results. We use the HRF, CHASE_DB1 public dataset to validate the fundus vessel segmentation performance of the MINet model. Also, we merged these two public datasets with our collected Ultra-widefield fundus image (UWF) data as one dataset to test the generalization ability of the model. Experimental results show that MINet achieves an F1 score of 0.8324 on the microvessel segmentation task, achieving a high accuracy compared to the current mainstream models.

Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.

CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images.

Blood vessel segmentation in fundus images is a critical procedure in the diagnosis of ophthalmic diseases. Recent deep learning methods achieve high accuracy in vessel segmentation but still face the challenge to segment the microvascular and detect the vessel boundary. This is due to the fact that common Convolutional Neural Networks (CNN) are unable to preserve rich spatial information and a large receptive field simultaneously. Besides, CNN models for vessel segmentation usually are trained by equal pixel level cross-entropy loss, which tend to miss fine vessel structures. In this paper, we propose a novel Context Spatial U-Net (CSU-Net) for blood vessel segmentation. Compared with the other U-Net based models, we design a two-channel encoder: a context channel with multi-scale convolution to capture more receptive field and a spatial channel with large kernel to retain spatial information. Also, to combine and strengthen the features extracted from two paths, we introduce a feature fusion module (FFM) and an attention skip module (ASM). Furthermore, we propose a structure loss, which adds a spatial weight to cross-entropy loss and guide the network to focus more on the thin vessels and boundaries. We evaluated this model on three public datasets: DRIVE, CHASE-DB1 and STARE. The results show that the CSU-Net achieves higher segmentation accuracy than the current state-of-the-art methods.

BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation

Background and objectiveThe condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results. MethodsIn this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images. ResultsThe proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments. ConclusionsBTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at: https://github.com/guomugong/BTS-DSN.

An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

Algorithms for retinal vessel segmentation are powerful tools in automatic tracking systems for early detection of ophthalmological and cardiovascular diseases, and for biometric identification. In order to create more robust and reliable systems, the algorithms need to be accurately evaluated to certify their ability to emulate specific human expertise. The main contribution of this paper is an unsupervised method to detect blood vessels in fundus images using a coarse-to-fine approach. Our methodology combines Gaussian smoothing, a morphological top-hat operator, and vessel contrast enhancement for background homogenization and noise reduction. Here, statistics of spatial dependency and probability are used to coarsely approximate the vessel map with an adaptive local thresholding scheme. The coarse segmentation is then refined through curvature analysis and morphological reconstruction to reduce pixel mislabeling and better estimate the retinal vessel tree. The method was evaluated in terms of its sensitivity, specificity and balanced accuracy. Extensive experiments have been conducted on DRIVE and STARE public retinal images databases. Comparisons with state-of-the-art methods revealed that our method outperformed most recent methods in terms of sensitivity and balanced accuracy with an average of 0.7819 and 0.8702, respectively. Also, the proposed method outperformed state-of-the-art methods when evaluating only pathological images that is a more challenging task. The method achieved for this set of images an average of 0.7842 and 0.8662 for sensitivity and balanced accuracy, respectively. Visual inspection also revealed that the proposed approach effectively addressed main image distortions by reducing mislabeling of central vessel reflex regions and false-positive detection of pathological patterns. These improvements indicate the ability of the method to accurately approximate the vessel tree with reduced visual interference of pathological patterns and vessel-like structures. Therefore, our method has the potential for supporting expert systems in screening, diagnosis and treatment of ophthalmological diseases, and furthermore for personal recognition based on retinal profile matching.

Optic disc detection in retinal fundus images using gravitational law-based edge detection.

Diabetic retinopathy is one of the primary causes of vision loss worldwide. Early detection of the condition is critical for providing adequate treatment of this ailment to prevent vision loss. This detection is achieved by processing retinal fundus images. A key step in detecting diabetic retinopathy is identifying the optic disc in these images. The optic disc is similar in color and contrast to the exudates that indicate diabetic retinopathy. Hence, the optic disc has to be removed from the fundus image before exudates can be detected. Detecting the optic disc is also required in algorithms used for blood vessel segmentation in fundus images. Therefore, there is a need for approaches that accurately and quickly detect optic disc. This paper proposes a simple, deterministic, and time-efficient approach for optic disc detection by adapting an edge detection algorithm inspired by the gravitational law. Our method introduces novel pre- and post-detection steps that aim to increase the accuracy of the adapted detection method. In addition, a candidate selection technique is proposed to decrease the number of missed optic discs. The proposed methodology was found to have a detection rate of 100, 97.75, 92.90, and 95% for DRIVE, DiaRet, DMED, and STARE datasets, respectively, which is comparatively better than existing optic disc detection schemes. Experimental results showed an average running time of 0.40s per image, which is significantly lower than available methods published in the literature.

Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.

Learning fully-connected CRFs for blood vessel segmentation in retinal images.

In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at http://pages.saclay.inria.fr/ matthew.blaschko/projects/retina/.