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MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images
- Source :
- Computer Methods and Programs in Biomedicine. 143:67-74
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Background and objective Automatic osteosarcoma tumor segmentation on computed tomography (CT) images is a challenging problem, as tumors have large spatial and structural variabilities. In this study, an automatic tumor segmentation method, which was based on a fully convolutional networks with multiple supervised side output layers (MSFCN), was presented. Methods Image normalization is applied as a pre-processing step for decreasing the differences among images. In the frame of the fully convolutional networks, supervised side output layers were added to three layers in order to guide the multi-scale feature learning as a contracting structure, which was then able to capture both the local and global image features. Multiple feature channels were used in the up-sampling portion to capture more context information, for the assurance of accurate segmentation of the tumor, with low contrast around the soft tissue. The results of all the side outputs were fused to determine the final boundaries of the tumors. Results A quantitative comparison of the 405 osteosarcoma manual segmentation results from the CT images showed that the average Dice similarity coefficient (DSC), average sensitivity, average Hammoude distance (HM) and F1-measure were 87.80%, 86.88%, 19.81% and 0.908, respectively. It was determined that, when compared with the other learning-based algorithms (for example, the fully convolution networks (FCN), U-Net method, and holistically-nested edge detection (HED) method), the MSFCN had the best performances in terms of DSC, sensitivity, HM and F1-measure. Conclusion The results indicated that the proposed algorithm contributed to the fast and accurate delineation of tumor boundaries, which could potentially assist doctors in making more precise treatment plans.
- Subjects :
- Adult
Tomography Scanners, X-Ray Computed
Adolescent
Computer science
Normalization (image processing)
Contrast Media
Scale-space segmentation
Bone Neoplasms
Health Informatics
Context (language use)
02 engineering and technology
Sensitivity and Specificity
Convolutional neural network
Edge detection
030218 nuclear medicine & medical imaging
Convolution
Young Adult
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Computer vision
Segmentation
Child
Observer Variation
Osteosarcoma
business.industry
Reproducibility of Results
Computer Science Applications
Feature (computer vision)
020201 artificial intelligence & image processing
Neural Networks, Computer
Artificial intelligence
Tomography, X-Ray Computed
business
Feature learning
Algorithms
Software
Subjects
Details
- ISSN :
- 01692607
- Volume :
- 143
- Database :
- OpenAIRE
- Journal :
- Computer Methods and Programs in Biomedicine
- Accession number :
- edsair.doi.dedup.....186055dfa3f83d22e4ce718e5d780a4b