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Multiple supervised residual network for osteosarcoma segmentation in CT images
- Source :
- Computerized Medical Imaging and Graphics. 63:1-8
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Automatic and accurate segmentation of osteosarcoma region in CT images can help doctor make a reasonable treatment plan, thus improving cure rate. In this paper, a multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation. Three supervised side output modules were added to the residual network. The shallow side output module could extract image shape features, such as edge features and texture features. The deep side output module could extract semantic features. The side output module could compute the loss value between output probability map and ground truth and back-propagate the loss information. Then, the parameters of residual network could be modified by gradient descent method. This could guide the multi-scale feature learning of the network. The final segmentation results were obtained by fusing the results output by the three side output modules. A total of 1900 CT images from 15 osteosarcoma patients were used to train the network and a total of 405 CT images from another 8 osteosarcoma patients were used to test the network. Results indicated that MSRN enabled a dice similarity coefficient (DSC) of 89.22%, a sensitivity of 88.74% and a F1-measure of 0.9305, which were larger than those obtained by fully convolutional network (FCN) and U-net. Thus, MSRN for osteosarcoma segmentation could give more accurate results than FCN and U-Net.
- Subjects :
- Similarity (geometry)
Computer science
Health Informatics
Residual
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Sensitivity (control systems)
Osteosarcoma
Ground truth
Radiological and Ultrasound Technology
business.industry
Pattern recognition
Image segmentation
Computer Graphics and Computer-Aided Design
030220 oncology & carcinogenesis
Neural Networks, Computer
Computer Vision and Pattern Recognition
Artificial intelligence
Tomography, X-Ray Computed
business
Gradient descent
Feature learning
Algorithms
Subjects
Details
- ISSN :
- 08956111
- Volume :
- 63
- Database :
- OpenAIRE
- Journal :
- Computerized Medical Imaging and Graphics
- Accession number :
- edsair.doi.dedup.....0339527a85a603fdab53e29866b0608c