Back to Search
Start Over
Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades
- Source :
- Medical physics. 46(1)
- Publication Year :
- 2018
-
Abstract
- Purpose Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks (CNN) Cascades. Methods CNN Cascades was a two-step, coarse-to-fine approach that consisted of a simple region detector (SRD) and a fine segmentation unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. Results The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. Conclusions The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.
- Subjects :
- Organs at Risk
Similarity (geometry)
Time Factors
business.industry
Computer science
Deep learning
Radiotherapy Planning, Computer-Assisted
Detector
Pattern recognition
General Medicine
Convolutional neural network
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Hausdorff distance
Region of interest
030220 oncology & carcinogenesis
Image Processing, Computer-Assisted
Segmentation
Artificial intelligence
Neural Networks, Computer
business
Tomography, X-Ray Computed
Subjects
Details
- ISSN :
- 24734209
- Volume :
- 46
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Medical physics
- Accession number :
- edsair.doi.dedup.....4c0123b2d2204090f3d4b9a33001bc42