Abstract
PurposeConvolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN.MethodsData of 100 patients (50 in supine and 50 in prone) with rectal cancer were collected for this study. We designed three sets of experiments for comparison: (a) segmentation using the model trained with data from the same orientation; (b) segmentation using the model trained with data from the opposite orientation; (c) segmentation using the model trained with data from both orientations. We performed fivefold cross‐validation. The performance was evaluated on segmentation of the clinical target volume (CTV), bladder, and femurs with Dice similarity coefficient (DSC) and Hausdorff distance (HD).ResultsCompared with models trained on cases positioned in the same orientation, the models trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) on CTV and bladder segmentation, but had comparable accuracy for femurs (P > 0.05). The average DSC values were 0.74 vs 0.84, 0.85 vs 0.88, and 0.91 vs 0.91 for CTV, bladder, and femurs, respectively. The corresponding HD values (mm) were 16.6 vs 14.6, 8.4 vs 8.1, and 6.3 vs 6.3, respectively. The models trained with data from both orientations have comparable accuracy (P > 0.05), with average DSC of 0.84, 0.88, and 0.91 and HD of 14.4, 8.1, and 6.3, respectively.ConclusionsOrientation affects the accuracy for CTV and bladder, but has negligible effect on the femurs. The model trained from data combining both orientations performs as well as a model trained with data from the same orientation for all the organs. These observations can offer guidance on the choice of training data for accurate segmentation.
Highlights
Segmentation of the organs‐at‐risk (OARs) and the tumor target is one of the key problems in the field of radiotherapy
The convolutional neural networks (CNN) segmentation models for clinical target volume (CTV) and bladder trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) than that trained with cases positioned in CTV Test on Bladder Test on Supine Prone Both
As for the models trained with data from both orientations, their segmentation accuracy was as good as models trained on data from the same orientation for all the three organs
Summary
Segmentation of the organs‐at‐risk (OARs) and the tumor target is one of the key problems in the field of radiotherapy. Computer‐assisted automated methods have the potential to reduce the inter‐ and intra‐ observer variability and relieve physicians from the labor‐intensive contouring workload. Such problems have been addressed in clinical applications using “atlas‐based” automated segmentation software.[1–3]. Lustberg et al.[9] and Lavdas et al.[10] demonstrated that CNN contouring demonstrated promising results in CT and MR image segmentation as compared with atlas‐based methods. Ibragimov et al.[11] successfully applied CNN for OAR segmentation in the head and neck CT images. With the promising learning tools and the enhancement of computer hardware, deep learning will dramatically change the landscape of radiotherapy contouring.[13]
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