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Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi
- Source :
- Journal of Radiation Research
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
Abstract
- This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with regards to delineating normal lung excluding the trachea and main bronchi. A total of 232 non-small-cell lung cancer cases were examined. The computed tomography (CT) images of these cases were converted from Digital Imaging and Communications in Medicine (DICOM) Radiation Therapy (RT) formats to arrays of 32 × 128 × 128 voxels and input into both 2D and 3D U-Net, which are deep learning networks for semantic segmentation. The number of training, validation and test sets were 160, 40 and 32, respectively. Dice similarity coefficients (DSCs) of the test set were evaluated employing Smart SegmentationⓇ Knowledge Based Contouring (Smart segmentation is an atlas-based segmentation tool), as well as the 2D and 3D U-Net. The mean DSCs of the test set were 0.964 [95% confidence interval (CI), 0.960–0.968], 0.990 (95% CI, 0.989–0.992) and 0.990 (95% CI, 0.989–0.991) with Smart segmentation, 2D and 3D U-Net, respectively. Compared with Smart segmentation, both U-Nets presented significantly higher DSCs by the Wilcoxon signed-rank test (P
- Subjects :
- Wilcoxon signed-rank test
Computer science
Health, Toxicology and Mutagenesis
trachea
Bronchi
computer.software_genre
030218 nuclear medicine & medical imaging
03 medical and health sciences
DICOM
Imaging, Three-Dimensional
0302 clinical medicine
Voxel
Regular Paper
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Radiation treatment planning
Lung
Contouring
Radiation
business.industry
Deep learning
Pattern recognition
U-Net
semantic segmentation
Semantics
lung cancer
main bronchi
030220 oncology & carcinogenesis
Test set
Artificial intelligence
Tomography, X-Ray Computed
business
computer
Algorithms
Subjects
Details
- ISSN :
- 13499157 and 04493060
- Volume :
- 61
- Database :
- OpenAIRE
- Journal :
- Journal of Radiation Research
- Accession number :
- edsair.doi.dedup.....5f76419a8ce3c59a5aa8ecc9a4f1970c