A feature-based approach for atlas selection in automatic pelvic segmentation
Guoping Shan1,2, Xue Bai2, Yun Ge1
1School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.
Plos One
|January 30, 2025
Summary
A new atlas selection method, MAS-SAGA, improves automatic segmentation accuracy and efficiency for clinical tasks like radiotherapy planning. It outperforms conventional methods and reduces computation time for medical image analysis.
Area of Science:
- Medical Image Analysis
- Computational Anatomy
- Radiotherapy Planning
Background:
- Accurate automatic segmentation is crucial for clinical applications, including radiotherapy.
- Existing atlas-based segmentation methods face limitations due to insufficient atlas data and computational constraints.
Purpose of the Study:
- To propose and evaluate a novel atlas selection procedure (MAS-SAGA) for enhanced multi-atlas-based segmentation.
- To compare the performance of feature-based (MAS-FASA) and similarity-based (MAS-SIM) atlas selection methods.
Main Methods:
- Developed the MAS-SAGA approach, integrating image similarity and volume features for atlas selection.
- Utilized a dataset of anonymized female pelvic CT images for segmentation of bladder and rectum.
- Employed a three-fold cross-validation strategy to assess segmentation accuracy and computational efficiency.
Main Results:
- MAS-SAGA demonstrated superior performance over conventional multi-atlas-based segmentation (cMAS) in Dice Similarity Coefficient (DSC) and 95th Percentile Hausdorff Distance (95HD) for bladder and rectum.
- The proposed method significantly reduced computation time compared to cMAS.
- MAS-FASA identified different atlases than MAS-SIM, leading to overall improved segmentation outcomes.
Conclusions:
- The MAS-SAGA procedure offers a promising advancement for medical image segmentation, enhancing accuracy and efficiency.
- Feature-based atlas selection techniques show potential for improving the efficacy of multi-atlas segmentation algorithms.
