A Deep Learning-Based Automatic Contour Quality Assurance Pipeline for Complex Anatomy on MRI. Int J Radiat Oncol Biol Phys 2021 Nov 01;111(3S):e509
Date
10/28/2021Pubmed ID
34701623DOI
10.1016/j.ijrobp.2021.07.1398Scopus ID
2-s2.0-85120924942 (requires institutional sign-in at Scopus site)Abstract
PURPOSE/OBJECTIVE(S): Despite the recent significant improvement on deep learning (DL)-based auto-segmentation, review and editing on auto-segmented contours are often needed to avoid errors. Such a manual time-consuming process is not desirable, particularly for recently introduced MR-guided adaptive radiation therapy (MRgART). To eliminate this manual process, we have previously proposed automatic contour quality assurance (ACQA) methods to automatically verify contour accuracy and have demonstrated that the ACQA methods worked well for most of organs, except for complex anatomy (e.g., stomach, bowels). This study aims to develop a DL-based ACQA pipeline specifically for complex organs that have inherent anatomy variations and intensity heterogeneity on MRIs.
MATERIALS/METHODS: The proposed pipeline includes the following major parts: 1) image pre-processing consisting of bias correction, normalization, intensity clipping, gas/high intensity contents filling and image cropping, 2)generation of three-channel RGB images by combining 30% core, inner shell and 4mm outer shell, 3) data augmentation through randomized 3D and 2D rotation, scaling and translation, 4) abnormal detection using statistical geometrical features extracted from ground truth contours (geo-check), and 5) training of a convolutional neural network (CNN) image classification model using samples passing geo-check. The DL auto-segmented contours of stomach on T2-weighted abdominal MRIs from 54 patients were utilized (40 for training and five-fold cross validation and 14 for independent test). A slice was labeled as inaccurate if the contour on the slice has mean distance to agreement (MDA) ≥2 mm or 95% Hausdorff distance (HD) ≥10 mm as compared to the ground truth. The labels were then manually checked to avoid mislabeling. The model performance was evaluated using area under curve (AUC) from receiver operating characteristic curve (ROC), predicting accuracy and F1 score.
RESULTS: A total of 139,230 sample images were generated for model training and 4826 for testing. The percentage of accurate slices identified were 64% and 72%, respectively, for the training and testing datasets. The average AUC/accuracy/F1 were 0.94/0.88/0.94 for cross validation, and 0.9/0.85/0.9 and 0.92/0.88/0.92 for the DL without or with geo-check over all testing cases. For each testing patient, the accuracy/F1 score were in the range of 0.78-0.98/ 0.71-0.99 with a median value of 0.89/0.91. The execution time for a testing contour was less than 1 second on a I7 CPU and a GTX 1060 GPU.
CONCLUSION: The proposed DL-ACQA method can quickly and automatically identify accurate and inaccurate contour slices after auto-segmentation of complex anatomy, e.g., stomach. With further development, this method may be integrated into current workflow, facilitating efficient and accurate segmentation in routine treatment planning or MRgART.
