Name
Title | ||
|---|---|---|
MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. |
Abstract | ||
|---|---|---|
•It is a deep end-to-end network for medical image segmentation.•Multiple supervision side output layers were introduced to the network for guiding the multi-scale feature learning.•A large number of feature channels were used in the up-sampling portion in order to capture more context information.•The segmentation method achieved an average DSC of 87.80%, an average sensitivity of 86.88%, an average HM of 19.81%, and an F1-measure of 0.9080, these results are better than some existing studies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.cmpb.2017.02.013 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Multiple supervised networks,Osteosarcoma segmentation,Convolutional neural networks | Normalization (image processing),Computer vision,Scale-space segmentation,Convolution,Segmentation,Feature (computer vision),Computer science,Convolutional neural network,Edge detection,Artificial intelligence,Feature learning | Journal |
Volume | ISSN | Citations |
143 | 0169-2607 | 5 |
PageRank | References | Authors |
0.42 | 18 | 5 |