Back to Search
Start Over
BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.
- Source :
-
International Journal of Medical Informatics . Jun2019, Vol. 126, p105-113. 9p. - Publication Year :
- 2019
-
Abstract
- <bold>Background and Objective: </bold>The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results.<bold>Methods: </bold>In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images.<bold>Results: </bold>The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments.<bold>Conclusions: </bold>BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at: https://github.com/guomugong/BTS-DSN. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13865056
- Volume :
- 126
- Database :
- Academic Search Index
- Journal :
- International Journal of Medical Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 136017564
- Full Text :
- https://doi.org/10.1016/j.ijmedinf.2019.03.015