SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.
Comput Biol Med
; 163: 107132, 2023 09.
Article
en En
| MEDLINE
| ID: mdl-37343468
Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel segmentation. SegR-Net utilizes a combination of feature extraction and embedding, deep feature magnification, feature precision and interference, and dense multiscale feature fusion to generate accurate segmentation masks. The model consists of an encoder module that extracts high-level features from the input images and a decoder module that reconstructs the segmentation masks by combining features from the encoder module. The encoder module consists of a feature extraction and embedding block that enhances by dense multiscale feature fusion, followed by a deep feature magnification block that magnifies the retinal vessels. To further improve the quality of the extracted features, we use a group of two convolutional layers after each DFM block. In the decoder module, we utilize a feature precision and interference block and a dense multiscale feature fusion block (DMFF) to combine features from the encoder module and reconstruct the segmentation mask. We also incorporate data augmentation and pre-processing techniques to improve the generalization of the trained model. Experimental results on three fundus image publicly available datasets (CHASE_DB1, STARE, and DRIVE) demonstrate that SegR-Net outperforms state-of-the-art models in terms of accuracy, sensitivity, specificity, and F1 score. The proposed framework can provide more accurate and more efficient segmentation of retinal blood vessels in comparison to the state-of-the-art techniques, which is essential for clinical decision-making and diagnosis of various eye diseases.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Comput Biol Med
Año:
2023
Tipo del documento:
Article
Pais de publicación:
Estados Unidos