[1] Moltz J H, Bornemann L, Dicken V, et al. Segmentation of liver metastases in CT scans by adaptive thresholding and morpho-logical processing[C]//The MIDAS Journal-Grand Challenge Liver Tumor Segmentation (2008 MICCAI Workshop), 2008, 472: 195–222.
[2] Chang Y L, Li X B. Adaptive image region-growing[J]. IEEE Transactions on Image Processing, 1994, 3(6): 868–872.
[3] Pohle R, Toennies K D. Segmentation of medical images using adaptive region growing[J]. Proceedings of SPIE, 2001, 4322: 1337–1346.
[4] Oda M, Nakaoka T, Kitasaka T, et al. Organ segmentation from 3D abdominal CT images based on atlas selection and graph cut[C]//Proceedings of the Third International Conference on Abdominal Imaging: Computational and Clinical Applications, 2012, 7029: 181–188.
[5] Criminisi A, Shotton J, Robertson D, et al. Regression forests for efficient anatomy detection and localization in CT stu-dies[C]//International MICCAI Workshop, MCV 2010, 2011: 106–117.
[9] Jones J L, Xie X H, Essa E. Combining region-based and im-precise boundary-based cues for interactive medical image segmentation[J].International Journal for Numerical Methods in Biomedical Engineering, 2014, 30(12): 1649–1666.
[10] Tah A A, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool[J]. BMC Medical Imaging, 2015, 15(1): 29.
[11] Criminisi A, Robertson D, Konukoglu E, et al. Regression forests for efficient anatomy detection and localization in computed to-mography scans[J]. Medical Image Analysis, 2013, 17(8): 1293–1303.
[12] Shin H C, Orton M R, Collins D J, et al. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1930–1943.
[13] Wang Z, Yang J. Automated detection of diabetic retinopathy using deep convolutional neural networks[J]. Medical Physics, 2016, 43(6): 3406.
[14] Kooi T, Litjens G, Van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic le-sions[J]. Medical Image Analysis, 2017, 35: 303–312.
[16] Yu L Q, Yang X, Hao C, et al. Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images[C]//Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI-17), 2017: 66–72.
[17] Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network[J]. Pattern Recognition and Image Analysis, 2017, 27(3): 618–624.
[18] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmenta-tion[C]//2016 Fourth International Conference on 3D Vision (3DV), 2016: 565–571.
[19] Ren M. Learning a classification model for segmenta-tion[C]//Proceedings Ninth IEEE International Conference on Computer Vision, 2003: 10–17.
[20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Z]. arXiv:1409.1556, 2015.
[21] Aghaei F, Ross S R, Wang Y Z, et al. Implementation of a com-puter-aided detection tool for quantification of intracranial radi-ologic markers on brain CT images[C]//Proceedings Volume 10138, Medical Imaging 2017: Imaging Informatics for Health-care, Research, and Applications, 2017: 10138.
[22] Korez R, Ibragimov B, Likar B, et al. Interpolation-based shape-constrained deformable model approach for segmenta-tion of vertebrae from CT spine images[C]//Recent Advances in Computational Methods and Clinical Applications for Spine Im-aging, 2015: 235–240.
[23] Liu X M, Guo S X, Yang B T, et al. Automatic organ segmenta-tion for CT scans based on super-pixel and convolutional neural networks[J]. Journal of Digital Imaging, 2018, 31(5): 748–760.