A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs

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 degradat...

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Published inSheng wu yi xue gong cheng xue za zhi Vol. 37; no. 2; pp. 311 - 316
Main Authors Wu, Qingnan, Wang, Yunlai, Quan, Hong, Wang, Junjie, Gu, Shanshan, Yang, Wei, Ge, Ruigang, Liu, Jie, Ju, Zhongjian
Format Journal Article
LanguageChinese
Published China Sichuan Society for Biomedical Engineering 25.04.2020
Subjects
Algorithms
Artificial neural networks
Bladder
Computed tomography
Humans
Image processing
Image Processing, Computer-Assisted
Image segmentation
Imaging, Three-Dimensional
Intestine
Machine learning
Medical imaging
Metric space
Neural networks
Neural Networks, Computer
Optimization
Organs
Organs at Risk
Pelvis
Rectum
Small intestine
Spinal cord
Tomography, X-Ray Computed
Training
deep learning
multiple model fusion
convolutional neural networks
automatic segmentation
organ at risk delineation
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Summary: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 ac
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ISSN:1001-5515
DOI:10.7507/1001-5515.201809011