Quality assurance of organs-at-risk delineation in radiotherapy
arxiv(2024)
摘要
The delineation of tumor target and organs-at-risk is critical in the
radiotherapy treatment planning. Automatic segmentation can be used to reduce
the physician workload and improve the consistency. However, the quality
assurance of the automatic segmentation is still an unmet need in clinical
practice. The patient data used in our study was a standardized dataset from
AAPM Thoracic Auto-Segmentation Challenge. The OARs included were left and
right lungs, heart, esophagus, and spinal cord. Two groups of OARs were
generated, the benchmark dataset manually contoured by experienced physicians
and the test dataset automatically created using a software AccuContour. A
resnet-152 network was performed as feature extractor, and one-class support
vector classifier was used to determine the high or low quality. We evaluate
the model performance with balanced accuracy, F-score, sensitivity, specificity
and the area under the receiving operator characteristic curve. We randomly
generated contour errors to assess the generalization of our method, explored
the detection limit, and evaluated the correlations between detection limit and
various metrics such as volume, Dice similarity coefficient, Hausdorff
distance, and mean surface distance. The proposed one-class classifier
outperformed in metrics such as balanced accuracy, AUC, and others. The
proposed method showed significant improvement over binary classifiers in
handling various types of errors. Our proposed model, which introduces residual
network and attention mechanism in the one-class classification framework, was
able to detect the various types of OAR contour errors with high accuracy. The
proposed method can significantly reduce the burden of physician review for
contour delineation.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要