Segmenting Retinal Blood Vessels With Deep Neural Networks

By Paweł Liskowski, Krzysztof Krawiec. IEEE Transactions on Medical Imaging, 2016

840 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ab657a056195325116f056cbfdca48a483453e3d

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In Computer Vision and Pattern Recognition

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions (2019, neutral): “9755 Deep Model [16] 0.”
Fully Automated Deep Learning System for Bone Age Assessment (2017, methodology): “From using deep CNNs to detect patterns of interstitial lung disease on 2D patches of chest CTs [5] to segmenting the vascular network of the human eye on fundus photos [6], deep CNNs have proven enormously successful since they enable learning highly representative, layered, hierarchical abstractio”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, neutral): “In [36], several deep learning architectures have been tested for vessel segmentation.”
DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017, background): “They have also significantly improved performance in a variety of medical imaging applications [33], [34], such as classification of lung diseases and lymph nodes in CT images [35], [36], segmentation (pixel classification) of brain tissues [37] in MRI, vessel segmentation [38] in fundus images, and”
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation (2020, neutral): “[15] 2016 0.”
End-to-End Adversarial Retinal Image Synthesis (2018, methodology): “9755 AUC on the DRIVE test set, a result aligned with state-of-the-art methods for retinal vessel segmentation [21]–[24].”
Computer Methods and Programs in Biomedicine (positive): “On the other side, CNNs are trained to directly obtain vascular segmentation in [51] for retinal vessel segmentation in OCT angiography, in [38] for carotid segmentation in ultrasound images and in [47] for retinal segmentation in color fundus photography images.”
DUNet: A deformable network for retinal vessel segmentation (2018, methodology): “Liskowski et al. proposed a supervised segmentation architecture that used a Deep Neural Network with a large training dataset which was preprocessed via global contrast normalization, zero-phase whitening, geometric transformations and gamma corrections [32].”
Adversarial Deep Structural Networks for Mammographic Mass Segmentation (2016, neutral): “Liskowski and Krawiec trained deep convolutional network on a large dataset to do retinal segmentation, and obtained impressive results [18].”
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017, methodology): “Recent works have extended the CNN framework to complex medical image analysis, such as retinal blood vessels segmentation [39,40], retinal hemorrhage detection [41], brain tumor segmentation [42], and cerebral microbleeds detection [43].”
CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “To compare performance of the vessel detection, we compute two evaluation metrics, the sensitivity (Sen) and the accuracy (Acc), which are also calculated in [4] and [6].”
Weighted Res-UNet for High-Quality Retina Vessel Segmentation (2018, neutral): “[9] 0.”