Name
Title | ||
|---|---|---|
Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images. |
Abstract | ||
|---|---|---|
We have developed an automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of treatment planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT images. First, the PET images were registered with the treatment planning CT images through the diagnostic CT images of PET/CT. Second, six voxel-based features including voxel values and magnitudes of image gradient vectors were derived from each voxel in the planning CT and PET /CT image data sets. Finally, lung tumors were extracted by using a support vector machine (SVM), which learned 6 voxel-based features inside and outside each true tumor region determined by radiation oncologists. The results showed that the average DSCs for 3 and 6 features for three cases were 0.744 and 0.899, and thus the SVM may need 6 features to learn the distinguishable characteristics. The proposed method may be useful for assisting treatment planners in delineation of the tumor region. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610168 | EMBC |
Keywords | Field | DocType |
treatment planning computed tomography,computerised tomography,radiation oncologist,learning (artificial intelligence),18f-fluorodeoxyglucose,machine learning classifier,diagnostic ct image,voxel-based feature,lung,lung tumor,svm,support vector machine,gradient methods,positron emission tomography,image gradient vector,tumours,support vector machines,medical image processing,fdg-pet/ct images,planning,computed tomography,image segmentation,learning artificial intelligence | Voxel,Nuclear medicine,PET-CT,Image-guided radiation therapy,Data set,Computer science,Support vector machine,Radiation treatment planning,Tomography,Positron emission tomography | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hidetaka Arimura | 1 | 37 | 7.52 |
| Ze Jin | 2 | 0 | 1.35 |
| Yoshiyuki Shioyama | 3 | 1 | 1.50 |
| Katsumasa Nakamura | 4 | 1 | 0.68 |
| Taiki Magome | 5 | 0 | 0.34 |
| Masayuki Sasaki | 6 | 0 | 0.68 |