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
in Chinese
| WPRIM
| ID: wpr-828165
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Pelvis
/
Algorithms
/
Image Processing, Computer-Assisted
/
Tomography, X-Ray Computed
/
Neural Networks, Computer
/
Imaging, Three-Dimensional
/
Organs at Risk
Type of study:
Etiology study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2020
Type:
Article
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