A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 311-316, 2020.
Article
em Zh
| WPRIM
| ID: wpr-828165
Biblioteca responsável:
WPRO
ABSTRACT
When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to develop a Dense V-network for automatic segmentation of three-dimensional computed tomography (CT) images, in order to solve the problems of degradation and gradient disappearance of three-dimensional convolutional neural networks optimization as training samples are insufficient. This algorithm is applied to the delineation of pelvic endangered organs and we take three representative evaluation parameters to quantitatively evaluate the segmentation effect. The clinical result showed that the Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 (average was 0.9); Jaccard distance of these were within 2.3 (average was 0.18). Except for the small intestine, the Hausdorff distance of other organs were less than 0.9 cm (average was 0.62 cm). The Dense V-Network has been proven to achieve the accurate segmentation of pelvic endangered organs.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Pelve
/
Algoritmos
/
Processamento de Imagem Assistida por Computador
/
Tomografia Computadorizada por Raios X
/
Redes Neurais de Computação
/
Imageamento Tridimensional
/
Órgãos em Risco
Tipo de estudo:
Etiology_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2020
Tipo de documento:
Article