A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation

Dasgupta, Avijit; Singh, Sonam

doi:10.1109/isbi.2017.7950512

Cited by 216 publications

(151 citation statements)

References 17 publications

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“…Further developments are presented in [41], [42], where Fully Convolutional Networks (FCN) are used to segment retinal vessels in color fundus photography images. The fully connected layers are replaced by deconvolutional layers allowing to obtain a faster and more precise vessel localization with respect to approaches based on fully connected layer classification.…”

Section: B Supervisedmentioning

confidence: 99%

“…V-A) Oliveira et al [22] 2011 Liver CT Goceri et al [23] 2017 Liver MRI Bruyninckx et al [24] 2010 Liver CT Bruyninckx et al [25] 2009 Lung CT Asad et al [26] 2017 Retina CFP Mapayi et al [27] 2015 Retina CFP Sreejini et al [28] 2015 Retina CFP Cinsdikici et al [29] 2009 Retina CFP Al-Rawi et al [30] 2007 Retina CFP Hanaoka et al [31] 2015 Brain MRA Supervised machine learning Sironi et al [32] 2014 Brain Microscopy (Sec. V-B) Merkow et al [33] 2016 Cardiovascular and Lung CT and MRI Sankaran et al [34] 2016 Coronary CTA Schaap et al [35] 2011 Coronary CTA Zheng et al [36] 2011 Coronary CT Nekovei et al [37] 1995 Coronary CT Smistad et al [38] 2016 Femoral region, Carotid US Chu et al [39] 2016 Liver X-ray fluoroscopic Orlando et al [40] 2017 Retina CFP Dasgupta et al [41] 2017 Retina CFP Mo et al [42] 2017 Retina CFP Lahiri et al [43] 2017 Retina CFP Annunziata et al [44] 2016 Retina Microscopy Fu et al [45] 2016 Retina CFP Luo et al [46] 2016 Retina CFP Liskowski et al [47] 2016 Retina CFP Li et al [48] 2016 Retina CFP Javidi et al [49] 2016 Retina CFP Maninis et al [50] 2016 Retina CFP Prentasvic et al [51] 2016 Retina CT Wu et al [52] 2016 Retina CFP Annunziata et al [53] 2015 Retina Microscopy Annunziata et al [54] 2015 Retina Microscopy Vega et al [55] 2015 Retina CFP Wang et al [56] 2015 Retina CFP Fraz et al [57] 2014 Retina CFP Ganin et al [58] 2014 Retina CFP...…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: B Supervisedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

View full text Add to dashboard Cite

show abstract

“…The proposed method was validated using DRIVE and STARE dataset for retinal vessel segmentation task where the area under recall-precision curve reached up to 0.822 for DRIVE dataset and 0.831 for STARE dataset. In context of CNN-based approaches, remarkable performances have been achieved by Liskowski et al [89] with supremum area under curve (AUC) of 0.99 and accuracy of 95.33%, while AUC of 0.974 was achieved by Dasgupta and Singh [91] for automated retinal vessel segmentation.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

2018

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Abstract:Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.

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“…Acc AUC Sens Spec Melinščak [4] 0.9466 0.9749 --Fu [5] 0.9523 -0.7603 -Li [7] 0.9527 0.9738 0.7569 0.9816 Dasgupta [8] 0.9533 0.9744 0.7691 0.9801 Yan [9] 0.9542 0.9752 0.7653 0.9818 Liskowski [10] 0.9251 0.9738 0.9160 0.9241 CapsNet [11] 0.9292 0.9638 0.7614 0.9731 Proposed 0.9547 0.9750 0.7651 0.9818 on the training images and combined the test predictions of each fold in an ensemble. We evaluated our model using the following metrics: Accuracy (Acc), Area under the curve (AUC), Sensitivity (Sens), and Specificity (Spec).…”

Section: Methodsmentioning

confidence: 99%

“…Tetteh et al [6] used the Inception architecture without pooling layers to classify each pixel in an image patch and extracted vessel centerlines. CNN autoencoder networks also classify each pixel of an image patch (e.g., [7], [8], [9]). The recent approach by Yan et al [9] takes into account the thickness of vessels by jointly adapting segment-level and pixel-wise loss functions.…”

Section: Introductionmentioning

confidence: 99%

Inception Capsule Network for Retinal Blood Vessel Segmentation and Centerline Extraction

Kromm

Rohr

2019

Preprint

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Automatic segmentation and centerline extraction of retinal blood vessels from fundus image data is crucial for early detection of retinal diseases. We have developed a novel deep learning method for segmentation and centerline extraction of retinal blood vessels which is based on the Capsule network in combination with the Inception architecture. Compared to state-of-the-art deep convolutional neural networks, our method has much fewer parameters due to its shallow architecture and generalizes well without using data augmentation. We performed a quantitative evaluation using the DRIVE dataset for both vessel segmentation and centerline extraction. Our method achieved state-of-the-art performance for vessel segmentation and outperformed existing methods for centerline extraction.

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A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation

Cited by 216 publications

References 17 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Inception Capsule Network for Retinal Blood Vessel Segmentation and Centerline Extraction

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