Review
Copyright ©The Author(s) 2021.
Artif Intell Gastroenterol. Apr 28, 2021; 2(2): 10-26
Published online Apr 28, 2021. doi: 10.35712/aig.v2.i2.10
Table 1 Target volume and organs at risk contouring with artificial intelligence
Ref.
Number of patients
Imaging method
Contouring
Artificial intelligence method
Results
Wang et al[54], 201893MR (3 Tesla, T2 -weighted)GTV, CTVCNNBetween deep learning-based autosegmentation and manual contouring DSC (P = 0.42), JSC (P = 0.35), HD (P = 0.079), and ASD (P = 0.16); Before postprocess process only in HD (P = 0.0027).
Trebeschi et al[55], 2017140Multiparametric MRI (1.5 Tesla, T2- weighted)GTVCNNAccording to CNN and both radiologists in independent validation data set DSC: 0.68 and 0.70; For both radiologists AUC: 0.99.
Song et al[56], 2020199CT (3 mm section thickness)CTV and OARCNNs (DeepLabv3+ and ResUNet)CTV segmentation better with DeepLabv3+ than ResUNet (volumetric DSC, 0.88 vs 0.87, P = 0.0005; surface DSC, 0.79 vs 0.78, P = 0.008); DeepLabv3+ model segmentation was better in the small intestine, with the ResUNet model, bladder and femoral heads segmentation results were better. In both models, the OAR manual correction time was 4 min.
Men et al[60], 2017278CT (5 mm section thickness)CTV and OARCNN (DDCNN)DSC values; CTV: 87.7%, bladder: 93.4%, left femoral head: 92.1%, right femoral head: 92.3%, small intestine: 65.3%, colon 61.8%.
Men et al[61], 2018100CT (3 mm section thickness)CTV and OARCNNCTV and bladder contouring were better in the model trained in the same position than the model trained in a different position (P < 0.05). No statistically significant difference between femoral heads (P > 0.05). No statistical difference between accuracy rates in CTV, bladder, and femoral heads segmentation in the model trained in both positions (P > 0.05).