A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images

By Qiaoliang Li, Bowei Feng, LinPei Xie, Ping Liang, Huisheng Zhang, Tianfu Wang. IEEE Transactions on Medical Imaging, 2016

534 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/1ad6a6f2b389bb63d9f7c683816f083a0795b90d

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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, unknown): “arison of the proposed network and the state-of-the-art methods on DRIVE dataset. Methods F1-Score Sensitivity Speciﬁcity Accuracy AUC COSFIRE ﬁlters [3] - 0.7655 0.97048 0.9442 0.9614 Cross-Modality [15] - 0.7569 0.9816 0.9527 0.9738 U-net [21] 0.8142 0.7537 0.9820 0.9531 0.9755 Deep Model [16] - 0”
Computer Methods and Programs in Biomedicine (unknown): “Cross-modality learning is used in [48] to segment retinal vessels.”
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017, background): “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].”
Adam Optimization Algorithm for Wide and Deep Neural Network (2019, background): “In 2016, [5] proposed a wide and deep neural network with strong induction ability to model the transformation, and an efficient training strategy.”
Deep learning in bioinformatics (2016, methodology): “In terms of biomedical imaging, DNNs have been applied in several research areas, including anomaly classification [122–124], segmentation [133], recognition [142, 143] and brain decoding [149, 150].”
Recurrent residual U-Net for medical image segmentation (2019, background): “In 2016, a cross-modality learning approach was proposed by Li et al. 56 and had reported AC of ∼ 0.9628 for STARE dataset, which had been previously the highest recorded result.”
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “In terms of the threshold selection for quality evaluation, we adopted the same method as described in [28] where the optimal threshold was set as the threshold maximizing Acc on the training set.”
DUNet: A deformable network for retinal vessel segmentation (2018, unknown): “Type Year PPV TPR TNR ACC AUC Azzopardi et al. [46] STA 2015 - 0.7716 0.9701 0.9497 0.9563 Li et al. [47] DNN 2015 - 0.7726 0.9844 0.9628 0.9879 Liskowski et al. [32] DNN 2016 - 0.8554 0.9862 0.9729 0.9928 Fu et al. [33] DNN 2016 - 0.7412 - 0.9585 - Roychowdhury et al. [49] STA 2017 - 0.7720 0.9730…”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, methodology): “We further assess the extendibility of the proposed three-stage deep learning model by conducting the cross-training evaluation as in [34].”
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image (2018, background): “For example, Convolutional Neural Networks (CNNs) learn hierarchical features from raw image data and are demonstrated to achieve a high performance on retinal vessel detection [23], [24].”