[1] [1] Yang N, Han Z, Li Z, et al. Automated pancreas segmentation using recurrent adversarial learning［C］∥ 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid,Spain:IEEE,2018:927-934.

[2] [2] Karasawa K, Kitasaka T, Oda M, et al. Structure specific atlas generation and its application to pancreas segmentation from contrasted abdominal CT volumes［C］∥ International MICCAI Workshop on Medical Computer Vision. Munich,Germany: Springer,2015:47-56.

[3] [3] Oda M, Shimizu N, Karasawa K, et al. Regression Forest-based atlas localization and direction specific atlas generation for pancreas segmentation［C］∥ International Conference on Medical Image Computing and Computer-Assisted Intervention.Athens,Greece:Springer,2016:556-563.

[4] [4] Karasawa K, Oda M, Kitasaka T, et al. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure［J］. Medical Image Analysis,2017,39:18-28.

[5] [5] Ye C, Ma T, Dan W, et al. Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools［J］. Plos One,2018,13(7):1-16.

[6] [6] Aganj Iman, Fischl Bruce. Multi-Atlas image soft segmentation via computation of the expected label value［J］. IEEE Transactions on Medical Imaging,2021,40(6):1702-1710.

[7] [7] Wang H, Suh J W, Das S R, et al. Multi-atlas segmentation with joint label fusion［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(3):611-623.

[8] [8] Bai, W, Shi, et al. A probabilistic Patch-based label fusion model for Multi-atlas segmentation with registration refinement: application to cardiac MR images［J］. IEEE Transactions on Medical Imaging,2013,32(7):1302-1315.

[9] [9] Asman A J, Landman B A. Hierarchical performance estimation in the statistical label fusion framework［J］. Medical Image Analysis,2014,18(7):1870-1881.

[10] [10] Sun L, Zu C, Shao W, et al. Reliability-based robust Multi-atlas label fusion for brain MRI segmentation［J］. Artificial Intelligence in Medicine,2019,96:12-24.

[11] [11] Saito A, Nawano S, Shimizu A. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs［J］. Medical Image Analysis,2016,28:46-65.

[12] [12] Okada T, Linguraru M G, Hori M, et al. Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors［J］. Medical Image Analysis,2015,26(1):1-18.

[14] [14] Yogarajah P, Condell J, Curran K, et al. A dynamic threshold approach for skin segmentation in color images［J］. International Journal of Biometrics,2012,4(1):38-55.

[15] [15] Shi Changfa, Xian Min, Zhou Xiancheng, et al. Multi-slice Low-rank tensor decomposition based Multi-atlas segmentation: application to automatic pathological liver CT segmentation［J］. Medical Image Analysis,2021,73:102152.

[16] [16] Clark, Vendt, Smith, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository［J］. Journal of Digital Imaging,2013,26(6):1045-1057.

[17] [17] Beare R, Lowekamp B, Yaniv Z. Image segmentation, registration and characterization in R with simpleITK［J］. Journal of Statistical Software,2018,86(8):1-35.

[18] [18] Awate S P, Zhu P, Whitaker R T. How many templates does it take for a good segmentation∶error analysis in multiatlas segmentation as a function of database size［C］∥ International Workshop on Multimodal Brain Image Analysis. Nice, France:Springer,2012:103-114.

[21] [21] Iglesias J E, Sabuncu M R. Multi-Atlas segmentation of biomedical images: A survey［J］. Medical Image Analysis,2015,24(1):205-219.