Retinal Blood Vessel Segmentation Using a Fully Convolutional Network – Transfer Learning from Patch- to Image-Level

2018 ◽  
pp. 170-178 ◽  
Author(s):  
Taibou Birgui Sekou ◽  
Moncef Hidane ◽  
Julien Olivier ◽  
Hubert Cardot
Keyword(s):  
Blood Vessel ◽  
Transfer Learning ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Blood Vessel Segmentation

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection

2020 ◽  
Vol 127 ◽  
pp. 104049
Author(s):  
José Escorcia-Gutierrez ◽  
Jordina Torrents-Barrena ◽  
Margarita Gamarra ◽  
Pedro Romero-Aroca ◽  
Aida Valls ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Foveal Avascular Zone ◽  
Retinal Blood Vessel ◽  
Shape Constraints ◽  
Blood Vessel Segmentation ◽  
Avascular Zone
Sign in / Sign up

Export Citation Format

Share Document