Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks.
J Digit Imaging
; 35(3): 459-468, 2022 06.
Article
em En
| MEDLINE
| ID: mdl-35132523
ABSTRACT
The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to generate accurate segmentation masks for gastric lesions. The proposed segmentation network adds residual blocks to the encoding and decoding path of U-Net. The cascaded dilated convolution is also added at the bottleneck of U-Net. The residual connection promotes information propagation, while dilated convolution integrates multi-scale context information. Meanwhile, a discriminator is used to distinguish the generated and real segmentation masks. The proposed discriminator is a Markov discriminator (Patch-GAN), which discriminates each [Formula see text] matrix in the image. In the process of network training, the adversary training mechanism is used to iteratively optimize the generator and the discriminator until they converge at the same time. The experimental results show that the dice, accuracy, and recall are 86.6%, 91.9%, and 87.3%, respectively. These metrics are significantly better than the existing models, which proves the effectiveness of this method and can meet the needs of clinical diagnosis and treatment.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
Limite:
Humans
Idioma:
En
Revista:
J Digit Imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
/
RADIOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China