Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

Soomro, Toufique Ahmed; Afifi, Ahmed J.; Gao, Junbin; Hellwich, Olaf; Khan, Md. Ashfaquzzaman; Paul, Manoranjan; Zheng, Lihong

doi:10.1109/dicta.2017.8227413

Cited by 44 publications

(31 citation statements)

References 26 publications

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“…Year Sensitivity Specificity Accuracy 2nd human observer Not available 0.8952 0.9385 0.9349 Hoover et al [8] 2000 0.6734 0.9568 0.9267 Jiang and Mojon [33] 2003 Not available Not available 0.9009 Martinez-Perez et al [31] 2007 0.7506 0.9569 0.9410 Al-Rawi et al [29] 2007 Not available Not available 0.9090 Palomera-Pérez et al [32] 2010 0.769 0.944 0.926 Budai et al [30] 2013 0.5800 0.9820 0.9370 Chakraborti et al [2] 2014 0.6786 0.9586 0.9379 Soomro et al [20] 2017 0.7480 0.9220 0.9480 Abdallah et al [17] 2018 0.6801 0.9711 0.9388 Leopold et al [21] 2019 0.6433 0.9472 0.9045 Proposed method 2019 0.7581 0.9550 0.9401…”

Section: Methodsmentioning

confidence: 99%

“…A u-net architecture is proposed in [19], which requires very few annotated images. Soomro et al [20] presented a method using deep conventional neural networks along with hysteresis threshold method for accurate detection of the narrowly low-contrast vessels. Leopold et al [21] proposed an efficient depth method for automatic segmentation of fundus morphology called Pix-elBNN, which can be well implemented even in the case of severe information loss.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

Gao

Chen

Lin

2020

BioMed Research International

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The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

Gao

Chen

Lin

2020

BioMed Research International

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“…Due to the consistency of the tables, the Table XIII below does not contain these parameters AUC in articles Soomro et al [101], Chudzik et al [103], Hajabdollahi et al [102], Guo et al [104], Sengür et al [110], Feng et al [112], Soomro et al [114] and AU ROC parameter in articles Mo et al [108], Lahiri et al [109], Wang et al [132], Wu et al [125] and PPV in article Feng et al [112] and visual comparison in article Gu et al [133]. It contains only Acc, Se and Sp.…”

Section: H Supervised Learning Algorithms and Deep Learningmentioning

confidence: 99%

Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

2020

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“…In the second step, deep-learning-based semantic segmentation was applied to extract the vessels. Finally, post-processing was used to refine the segmentation [46]. Guo et al proposed a multi-level and multi-scale approach, where short-cut connections were used for the semantic segmentation of vessels and semantic information was passed to forward layers to improve the performance [47].…”

Section: Related Workmentioning

confidence: 99%

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Arsalan

Owais

Mahmood

et al. 2019

JCM

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Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

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Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

Cited by 44 publications

References 26 publications

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

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