[1] [1] CASATI M, PIFFER S, CALUSI S, et al. Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images[J]. Journal of Applied Clinical Medical Physics, 2022, 23(3): e13507.

[2] [2] LIU P, HAN H, DU Y, et al. Deep learning to segment pelvic bones: large-scale CT datasets and baseline models[J]. International Journal of Computer Assisted Radiology and Surgery, 2021, 16: 749-756.

[3] [3] YANG F, SUN Q, JIN H, et al. Superpixel segmentation with fully convolutional networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 13964-13973.

[4] [4] SHAFIQ M, GU Z. Deep residual learning for image recognition: a survey[J]. Applied Sciences, 2022, 12(18): 8972.

[5] [5] LIU S, SHEN J, HUANG S. Object detection in seriously degraded images with unbalanced training samples[J]. Optoelectronics Letters, 2021, 17(9): 564-571.

[7] [7] HAN K, WANG Y, CHEN H, et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(1): 87-110.

[10] [10] DENG Y, WANG L, ZHAO C, et al. A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images[J]. Medical & Biological Engineering & Computing, 2022, 60(5): 1417-1429.

[11] [11] WANG T, XIA W, HUANG Y, et al. DAN-Net: dual-domain adaptive-scaling non-local network for CT metal artifact reduction[J]. Physics in Medicine & Biology, 2021, 66(15): 155009.

[12] [12] PENG C, LI B, LIANG P, et al. A cross-domain metal trace restoring network for reducing X-ray CT metal artifacts[J]. IEEE Transactions on Medical Imaging, 2020, 39(12): 3831-3842.

[13] [13] YU L, ZHANG Z, LI X, et al. Deep sinogram completion with image prior for metal artifact reduction in CT images[J]. IEEE Transactions on Medical Imaging, 2021, 40(1): 228-238.

[14] [14] YANG J, LI C, DAI X, et al. Focal modulation networks[C]//Advances in Neural Information Processing Systems 35, November 28-December 9, 2022, New Orleans, Louisiana, USA. New York: Curran AssociatesInc, 2022: 4203-4217.

[15] [15] LIANG T, CHU X, LIU Y, et al. CBNet: A composite backbone network architecture for object detection[J]. IEEE Transactions on Image Processing, 2022, 31: 6893-6906.

[16] [16] MUKHOTI J, KULHARIA V, SANYAL A, et al. Calibrating deep neural networks using focal loss[C]//Advances in Neural Information Processing Systems 33, December 6-12, 2020, Virtual. New York: Curran AssociatesInc, 2020: 15288-15299.

[17] [17] ZHAO R, QIAN B, ZHANG X, et al. Rethinking dice loss for medical image segmentation[C]//2020 IEEE International Conference on Data Mining, November 17-20, 2020, SorrentoItaly, Italy. New York: IEEE, 2020: 17-20.

[18] [18] ISENSEE F, JGER P F, KOHL S, et al. Automated design of deep learning methods for biomedical image segmentation[EB/OL].(2020-04-02) [2023-03-09]. https://arxiv.org/abs/1904.08128.

[19] [19] WANG J, LIU X. Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network[J]. Computer Methods and Programs in Biomedicine, 2021, 207: 106210.