BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.
Int J Med Inform
; 126: 105-113, 2019 06.
Article
em En
| MEDLINE
| ID: mdl-31029251
ABSTRACT
BACKGROUND AND OBJECTIVE:
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.METHODS:
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.RESULTS:
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.CONCLUSIONS:
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Vasos Retinianos
/
Redes Neurais de Computação
/
Aprendizado de Máquina Supervisionado
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Int J Med Inform
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
China