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RWRM: Residual Wasserstein regularization model for image restoration
Automatic segmentation of the femur and tibia bones from Xray images based on pure dilated residual UNet
1.  School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China 
2.  Tianjin Institute of Orthopaedics, Tianjin Hospital, Tianjin University, Tianjin 300211, China 
3.  Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China 
4.  School of Artificial Intelligence, Hebei Key Laboratory of Robot Perception and HumanRobot Interaction, Hebei University of Technology, Tianjin 300401, China 
Xray images of the lower limb bone are the most commonly used imaging modality for clinical studies, and segmentation of the femur and tibia in an Xray image is helpful for many medical studies such as diagnosis, surgery and treatment. In this paper, we propose a new approach based on pure dilated residual UNet for the segmentation of the femur and tibia bones. The proposed approach employs dilated convolution completely to increase the receptive field, in this way, we can make full use of the advantages of dilated convolution. We conducted experiments and evaluations on datasets provided by Tianjin hospital. Comparison with the classical Unet and FusionNet, our method has fewer parameters, higher accuracy, and converges more rapidly, which means the high performance of the proposed method.
References:
[1] 
S. Y. Ababneh, J. W. Prescott and M. N. Gurcan, Automatic graphcut based segmentation of bones from knee magnetic resonance images for osteoarthritis research, Medical Image Anal., 15 (2011), 438448. doi: 10.1016/j.media.2011.01.007. 
[2] 
V. Badrinarayanan, A. Kendall and R. Cipolla, Segnet: A deep convolutional encoderdecoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 2481–2495. doi: 10.1109/TPAMI.2016.2644615. 
[3] 
O. Bandyopadhyay, A. Biswas and B. B. Bhattacharya, Longbone fracture detection in digital xray images based on digitalgeometric techniques, Comput. Methods Programs Biomed., 123 (2016), 214. doi: 10.1016/j.cmpb.2015.09.013. 
[4] 
J. CarballidoGamio, et al., Automatic multiparametric quantification of the proximal femur with quantitative computed tomography, Quantitative Imaging in Medicine and Surgery, 5 (2015), 552568. 
[5] 
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell., 40 (2018), 834–848. doi: 10.1109/TPAMI.2017.2699184. 
[6] 
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox and O. Ronneberger, 3d unet: Learning dense volumetric segmentation from sparse annotation, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 2016  19th International Conference, Athens, Greece, October 1721, 2016, Proceedings, Part II, Lecture Notes in Computer Science, 9901, 2016,424–432. doi: 10.1007/9783319467238_49. 
[7] 
C. M. Deniz, S. Hallyburton, A. Welbeck, S. Honig, K. Cho and G. Chang, Segmentation of the proximal femur from MR images using deep convolutional neural networks, Sci. Rep., 8 (2018), 16485. doi: 10.1038/s41598018348176. 
[8] 
F. Ding, W. K. Leow and T. S. Howe, Automatic segmentation of femur bones in anteriorposterior pelvis xray images, in Computer Analysis of Images and Patterns, 12th International Conference, CAIP 2007, Vienna, Austria, August 2729, 2007, Proceedings (eds. W. G. Kropatsch, M. Kampel and A. Hanbury), Lecture Notes in Computer Science, 4673, Springer, 2007,205–212. doi: 10.1007/9783540742722_26. 
[9] 
L.H. Fan, J.G. Han, Y. Jia, C. Zhao and B. Yang, Segmentation of femurs in xray image with generative adversarial networks, DEStech Transactions on Engineering and Technology Research, 289–295. doi: 10.12783/dtetr/ecae2018/27745. 
[10] 
I. J. Goodfellow, et al., Generative adversarial nets, in Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 813 2014, Montreal, Quebec, Canada (eds. Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence and K. Q. Weinberger), 2014, 2672–2680 
[11] 
S. Guan, A. A. Khan, S. Sikdar and P. V. Chitnis, Fully dense unet for 2d sparse photoacoustic tomography artifact removal, preprint, arXiv: 1808.10848. doi: 10.1109/JBHI.2019.2912935. 
[12] 
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin and A. C. Courville, Improved training of wasserstein gans, in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 49 December 2017, Long Beach, CA, USA (eds. I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan and R. Garnett), 2017, 5767–5777 
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A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, 2012 doi: 10.1145/3065386. 
[17] 
H. Li, A. Zhygallo and B. H. Menze, Automatic brain structures segmentation using deep residual dilated unet, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries  4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I (eds. A. Crimi, S. Bakas, H. J. Kuijf, F. Keyvan, M. Reyes and T. van Walsum), Lecture Notes in Computer Science, 11383, Springer, 2018,385–393. doi: 10.1007/9783030117238_39. 
[18] 
M. Lin, Q. Chen and S. Yan, Network in network, in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 1416, 2014, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[19] 
M. Liu, T. Breuel and J. Kautz, Unsupervised imagetoimage translation networks, in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 49 December 2017, Long Beach, CA, USA (eds. I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan and R. Garnett), 2017,700–708 
[20] 
X. Liu, et al., Msdfnet: Multiscale deep fusion network for stroke lesion segmentation, IEEE Access, 7 (2019), 178486–178495. doi: 10.1109/ACCESS.2019.2958384. 
[21] 
J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 712, 2015 doi: 10.1109/CVPR.2015.7298965. 
[22] 
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang and S. P. Smolley, Least squares generative adversarial networks, in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 2229, 2017 doi: 10.1109/ICCV.2017.304. 
[23] 
F. Milletari, N. Navab and S. Ahmadi, Vnet: Fully convolutional neural networks for volumetric medical image segmentation, in Fourth International Conference on 3D Vision, 3DV 2016, Stanford, CA, USA, October 2528, 2016 doi: 10.1109/3DV.2016.79. 
[24] 
O. Oktay, et al., Attention unet: Learning where to look for the pancreas, preprint, arXiv: 1804.03999. 
[25] 
C. N. Öztürk and S. Albayrak, Automatic segmentation of cartilage in highfield magnetic resonance images of the knee joint with an improved voxelclassificationdriven regiongrowing algorithm using vicinitycorrelated subsampling, Comp. Bio. Med., 72 (2016), 90107. doi: 10.1016/j.compbiomed.2016.03.011. 
[26] 
T. T. Peng, et al., Detection of femur fractures in xray images, Master of Science Thesis, National University of Singapore. 
[27] 
A. Pries, P. J. Schreier, A. Lamm, S. Pede and J. Schmidt, Deep morphing: Detecting bone structures in fluoroscopic xray images with prior knowledge, preprint, arXiv: 1808.04441. 
[28] 
T. M. Quan, D. G. C. Hildebrand and W. Jeong, Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics, preprint, arXiv: 1612.05360. 
[29] 
O. Ronneberger, P. Fischer and T. Brox, Unet: Convolutional networks for biomedical image segmentation, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 201518th International Conference Munich, Germany, October 59, 2015, Proceedings, Part III (eds. N. Navab, J. Hornegger, W. M. W. III and A. F. Frangi), Lecture Notes in Computer Science, 9351, Springer, 2015,234–241. doi: 10.1007/9783319245744_28. 
[30] 
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford and X. Chen, Improved techniques for training gans, in Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 510, 2016, Barcelona, Spain (eds. D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon and R. Garnett), 2016, 2226–2234 
[31] 
P. Santhoshini, R. Tamilselvi and R. Sivakumar, Automatic segmentation of femur bone features and analysis of osteoporosis, Lecture Notes on Software Engineering, 194–198. doi: 10.7763/LNSE.2013.V1.44. 
[32] 
K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 79, 2015, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[33] 
R. Smith, Segmentation and fracture detection in xray images for traumatic pelvic injury., 
[34] 
C. StolojescuCrisan and S. Holban, An interactive xray image segmentation technique for bone extraction, in International WorkConference on Bioinformatics and Biomedical Engineering, IWBBIO 2014, Granada, Spain, April 79, 2014 (eds. I. Rojas and F. M. O. Guzman), Copicentro Editorial, 2014, 1164–1171 
[35] 
H. Sun, et al., Aunet: Attentionguided denseupsampling networks for breast mass segmentation in whole mammograms, Phys. Med. Biol., 65 (2020), 055005. doi: 10.1088/13616560/ab5745. 
[36] 
C. Szegedy, et al., Going deeper with convolutions, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 712, 2015 doi: 10.1109/CVPR.2015.7298594. 
[37] 
A. Tack, A. Mukhopadhyay and S. Zachow, Knee menisci segmentation using convolutional neural networks: Data from the osteoarthritis initiative, Osteoarthritis and Cartilage, 26 (2018), 680688. doi: 10.1016/j.joca.2018.02.907. 
[38] 
W. Wang, Y. Wang, Y. Wu, T. Lin, S. Li and B. Chen, Quantification of full left ventricular metrics via deep regression learning with contourguidance, IEEE Access, 7 (2019), 4791847928. doi: 10.1109/ACCESS.2019.2907564. 
[39] 
J. Wu, A. Belle, R. H. Hargraves, C. Cockrell, Y. Tang and K. Najarian, Bone segmentation and 3d visualization of CT images for traumatic pelvic injuries, Int. J. Imaging Syst. Technol., 24 (2014), 2938. doi: 10.1002/ima.22076. 
[40] 
X. Xiao, S. Lian, Z. Luo and S. Li, Weighted resunet for highquality retina vessel segmentation, in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 2018,327–331. 
[41] 
Y. Xue, T. Xu, H. Zhang, L. R. Long and X. Huang, Segan: Adversarial network with multiscale L 1 loss for medical image segmentation, Neuroinformatics, 16 (2018), 383392. doi: 10.1007/s120210189377x. 
[42] 
F. Yokota, T. Okada, M. Takao, N. Sugano, Y. Tada and Y. Sato, Automated segmentation of the femur and pelvis from 3d CT data of diseased hip using hierarchical statistical shape model of joint structure, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 2009, 12th International Conference, London, UK, September 2024, 2009, Proceedings, Part II (eds. G. Yang, D. J. Hawkes, D. Rueckert, J. A. Noble and C. J. Taylor), Lecture Notes in Computer Science, 5762, Springer, 2009,811–818. doi: 10.1007/9783642042713_98. 
[43] 
F. Yu and V. Koltun, Multiscale context aggregation by dilated convolutions, in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 24, 2016, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[44] 
K. Zhang, W. Lu and P. Marziliano, Automatic knee cartilage segmentation from multicontrast mr images using support vector machine classification with spatial dependencies, Magnetic Resonance Imaging, 31 (2013), 17311743. doi: 10.1016/j.mri.2013.06.005. 
[45] 
Z. Zhang, C. Duan, T. Lin, S. Zhou, Y. Wang and X. Gao, GVFOM: A novel external force for active contour based image segmentation, Inf. Sci., 506 (2020), 118. doi: 10.1016/j.ins.2019.08.003. 
[46] 
Y. Zhou, W. Huang, P. Dong, Y. Xia and S. Wang, Dunet: A dimensionfusion U shape network for chronic stroke lesion segmentation, preprint, arXiv: 1908.05104. doi: 10.1109/TCBB.2019.2939522. 
[47] 
J. Zhu, T. Park, P. Isola and A. A. Efros, Unpaired imagetoimage translation using cycleconsistent adversarial networks, in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 2229, 2017 doi: 10.1109/ICCV.2017.244. 
[48] 
Keras: Deep learning library for theano and tensorflow, https://github.com/kerasteam/keras, 2015. 
[49] 
Lableme, http://labelme.csail.mit.edu/Release3.0/. 
show all references
References:
[1] 
S. Y. Ababneh, J. W. Prescott and M. N. Gurcan, Automatic graphcut based segmentation of bones from knee magnetic resonance images for osteoarthritis research, Medical Image Anal., 15 (2011), 438448. doi: 10.1016/j.media.2011.01.007. 
[2] 
V. Badrinarayanan, A. Kendall and R. Cipolla, Segnet: A deep convolutional encoderdecoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 2481–2495. doi: 10.1109/TPAMI.2016.2644615. 
[3] 
O. Bandyopadhyay, A. Biswas and B. B. Bhattacharya, Longbone fracture detection in digital xray images based on digitalgeometric techniques, Comput. Methods Programs Biomed., 123 (2016), 214. doi: 10.1016/j.cmpb.2015.09.013. 
[4] 
J. CarballidoGamio, et al., Automatic multiparametric quantification of the proximal femur with quantitative computed tomography, Quantitative Imaging in Medicine and Surgery, 5 (2015), 552568. 
[5] 
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell., 40 (2018), 834–848. doi: 10.1109/TPAMI.2017.2699184. 
[6] 
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox and O. Ronneberger, 3d unet: Learning dense volumetric segmentation from sparse annotation, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 2016  19th International Conference, Athens, Greece, October 1721, 2016, Proceedings, Part II, Lecture Notes in Computer Science, 9901, 2016,424–432. doi: 10.1007/9783319467238_49. 
[7] 
C. M. Deniz, S. Hallyburton, A. Welbeck, S. Honig, K. Cho and G. Chang, Segmentation of the proximal femur from MR images using deep convolutional neural networks, Sci. Rep., 8 (2018), 16485. doi: 10.1038/s41598018348176. 
[8] 
F. Ding, W. K. Leow and T. S. Howe, Automatic segmentation of femur bones in anteriorposterior pelvis xray images, in Computer Analysis of Images and Patterns, 12th International Conference, CAIP 2007, Vienna, Austria, August 2729, 2007, Proceedings (eds. W. G. Kropatsch, M. Kampel and A. Hanbury), Lecture Notes in Computer Science, 4673, Springer, 2007,205–212. doi: 10.1007/9783540742722_26. 
[9] 
L.H. Fan, J.G. Han, Y. Jia, C. Zhao and B. Yang, Segmentation of femurs in xray image with generative adversarial networks, DEStech Transactions on Engineering and Technology Research, 289–295. doi: 10.12783/dtetr/ecae2018/27745. 
[10] 
I. J. Goodfellow, et al., Generative adversarial nets, in Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 813 2014, Montreal, Quebec, Canada (eds. Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence and K. Q. Weinberger), 2014, 2672–2680 
[11] 
S. Guan, A. A. Khan, S. Sikdar and P. V. Chitnis, Fully dense unet for 2d sparse photoacoustic tomography artifact removal, preprint, arXiv: 1808.10848. doi: 10.1109/JBHI.2019.2912935. 
[12] 
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin and A. C. Courville, Improved training of wasserstein gans, in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 49 December 2017, Long Beach, CA, USA (eds. I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan and R. Garnett), 2017, 5767–5777 
[13] 
K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 2730, 2016 doi: 10.1109/CVPR.2016.90. 
[14] 
G. Huang, Z. Liu, L. van der Maaten and K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 2126, 2017 doi: 10.1109/CVPR.2017.243. 
[15] 
R. Jiang, J. Meng and P. Babyn, Xray image segmentation using active contour model with global constraints, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, (2007), 240–245. 
[16] 
A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, 2012 doi: 10.1145/3065386. 
[17] 
H. Li, A. Zhygallo and B. H. Menze, Automatic brain structures segmentation using deep residual dilated unet, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries  4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I (eds. A. Crimi, S. Bakas, H. J. Kuijf, F. Keyvan, M. Reyes and T. van Walsum), Lecture Notes in Computer Science, 11383, Springer, 2018,385–393. doi: 10.1007/9783030117238_39. 
[18] 
M. Lin, Q. Chen and S. Yan, Network in network, in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 1416, 2014, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[19] 
M. Liu, T. Breuel and J. Kautz, Unsupervised imagetoimage translation networks, in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 49 December 2017, Long Beach, CA, USA (eds. I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan and R. Garnett), 2017,700–708 
[20] 
X. Liu, et al., Msdfnet: Multiscale deep fusion network for stroke lesion segmentation, IEEE Access, 7 (2019), 178486–178495. doi: 10.1109/ACCESS.2019.2958384. 
[21] 
J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 712, 2015 doi: 10.1109/CVPR.2015.7298965. 
[22] 
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang and S. P. Smolley, Least squares generative adversarial networks, in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 2229, 2017 doi: 10.1109/ICCV.2017.304. 
[23] 
F. Milletari, N. Navab and S. Ahmadi, Vnet: Fully convolutional neural networks for volumetric medical image segmentation, in Fourth International Conference on 3D Vision, 3DV 2016, Stanford, CA, USA, October 2528, 2016 doi: 10.1109/3DV.2016.79. 
[24] 
O. Oktay, et al., Attention unet: Learning where to look for the pancreas, preprint, arXiv: 1804.03999. 
[25] 
C. N. Öztürk and S. Albayrak, Automatic segmentation of cartilage in highfield magnetic resonance images of the knee joint with an improved voxelclassificationdriven regiongrowing algorithm using vicinitycorrelated subsampling, Comp. Bio. Med., 72 (2016), 90107. doi: 10.1016/j.compbiomed.2016.03.011. 
[26] 
T. T. Peng, et al., Detection of femur fractures in xray images, Master of Science Thesis, National University of Singapore. 
[27] 
A. Pries, P. J. Schreier, A. Lamm, S. Pede and J. Schmidt, Deep morphing: Detecting bone structures in fluoroscopic xray images with prior knowledge, preprint, arXiv: 1808.04441. 
[28] 
T. M. Quan, D. G. C. Hildebrand and W. Jeong, Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics, preprint, arXiv: 1612.05360. 
[29] 
O. Ronneberger, P. Fischer and T. Brox, Unet: Convolutional networks for biomedical image segmentation, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 201518th International Conference Munich, Germany, October 59, 2015, Proceedings, Part III (eds. N. Navab, J. Hornegger, W. M. W. III and A. F. Frangi), Lecture Notes in Computer Science, 9351, Springer, 2015,234–241. doi: 10.1007/9783319245744_28. 
[30] 
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford and X. Chen, Improved techniques for training gans, in Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 510, 2016, Barcelona, Spain (eds. D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon and R. Garnett), 2016, 2226–2234 
[31] 
P. Santhoshini, R. Tamilselvi and R. Sivakumar, Automatic segmentation of femur bone features and analysis of osteoporosis, Lecture Notes on Software Engineering, 194–198. doi: 10.7763/LNSE.2013.V1.44. 
[32] 
K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 79, 2015, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[33] 
R. Smith, Segmentation and fracture detection in xray images for traumatic pelvic injury., 
[34] 
C. StolojescuCrisan and S. Holban, An interactive xray image segmentation technique for bone extraction, in International WorkConference on Bioinformatics and Biomedical Engineering, IWBBIO 2014, Granada, Spain, April 79, 2014 (eds. I. Rojas and F. M. O. Guzman), Copicentro Editorial, 2014, 1164–1171 
[35] 
H. Sun, et al., Aunet: Attentionguided denseupsampling networks for breast mass segmentation in whole mammograms, Phys. Med. Biol., 65 (2020), 055005. doi: 10.1088/13616560/ab5745. 
[36] 
C. Szegedy, et al., Going deeper with convolutions, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 712, 2015 doi: 10.1109/CVPR.2015.7298594. 
[37] 
A. Tack, A. Mukhopadhyay and S. Zachow, Knee menisci segmentation using convolutional neural networks: Data from the osteoarthritis initiative, Osteoarthritis and Cartilage, 26 (2018), 680688. doi: 10.1016/j.joca.2018.02.907. 
[38] 
W. Wang, Y. Wang, Y. Wu, T. Lin, S. Li and B. Chen, Quantification of full left ventricular metrics via deep regression learning with contourguidance, IEEE Access, 7 (2019), 4791847928. doi: 10.1109/ACCESS.2019.2907564. 
[39] 
J. Wu, A. Belle, R. H. Hargraves, C. Cockrell, Y. Tang and K. Najarian, Bone segmentation and 3d visualization of CT images for traumatic pelvic injuries, Int. J. Imaging Syst. Technol., 24 (2014), 2938. doi: 10.1002/ima.22076. 
[40] 
X. Xiao, S. Lian, Z. Luo and S. Li, Weighted resunet for highquality retina vessel segmentation, in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 2018,327–331. 
[41] 
Y. Xue, T. Xu, H. Zhang, L. R. Long and X. Huang, Segan: Adversarial network with multiscale L 1 loss for medical image segmentation, Neuroinformatics, 16 (2018), 383392. doi: 10.1007/s120210189377x. 
[42] 
F. Yokota, T. Okada, M. Takao, N. Sugano, Y. Tada and Y. Sato, Automated segmentation of the femur and pelvis from 3d CT data of diseased hip using hierarchical statistical shape model of joint structure, in Medical Image Computing and ComputerAssisted Intervention  MICCAI 2009, 12th International Conference, London, UK, September 2024, 2009, Proceedings, Part II (eds. G. Yang, D. J. Hawkes, D. Rueckert, J. A. Noble and C. J. Taylor), Lecture Notes in Computer Science, 5762, Springer, 2009,811–818. doi: 10.1007/9783642042713_98. 
[43] 
F. Yu and V. Koltun, Multiscale context aggregation by dilated convolutions, in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 24, 2016, Conference Track Proceedings (eds. Y. Bengio and Y. LeCun), preprint 
[44] 
K. Zhang, W. Lu and P. Marziliano, Automatic knee cartilage segmentation from multicontrast mr images using support vector machine classification with spatial dependencies, Magnetic Resonance Imaging, 31 (2013), 17311743. doi: 10.1016/j.mri.2013.06.005. 
[45] 
Z. Zhang, C. Duan, T. Lin, S. Zhou, Y. Wang and X. Gao, GVFOM: A novel external force for active contour based image segmentation, Inf. Sci., 506 (2020), 118. doi: 10.1016/j.ins.2019.08.003. 
[46] 
Y. Zhou, W. Huang, P. Dong, Y. Xia and S. Wang, Dunet: A dimensionfusion U shape network for chronic stroke lesion segmentation, preprint, arXiv: 1908.05104. doi: 10.1109/TCBB.2019.2939522. 
[47] 
J. Zhu, T. Park, P. Isola and A. A. Efros, Unpaired imagetoimage translation using cycleconsistent adversarial networks, in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 2229, 2017 doi: 10.1109/ICCV.2017.244. 
[48] 
Keras: Deep learning library for theano and tensorflow, https://github.com/kerasteam/keras, 2015. 
[49] 
Lableme, http://labelme.csail.mit.edu/Release3.0/. 
Block Type  Convolutional Layer  Receptive Field 
standard block 1  conv1_1  11+1 
dilated rate = 1  conv1_2  31+1 
residual block 2  conv2_1  51+2 
dilated rate = 2  conv2_2  91+2 
residual block 3  conv3_1  131+4 
dilated rate = 4  conv3_2  211+4 
residual block 4  conv4_1  291+8 
dilated rate = 8  conv4_2  451+8 
residual block 5  conv5_1  611+16 
dilated rate = 16  conv5_2  931+16 
residual block 6  conv6_1  1251+32 
dilated rate = 32  conv6_2  1891+32 
residual block 7  conv7_1  2531+32 
dilated rate = 32  conv7_2  3171+32 
residual block 8  conv8_1  3811+32 
dilated rate = 32  conv8_2  4451+32 
Block Type  Convolutional Layer  Receptive Field 
standard block 1  conv1_1  11+1 
dilated rate = 1  conv1_2  31+1 
residual block 2  conv2_1  51+2 
dilated rate = 2  conv2_2  91+2 
residual block 3  conv3_1  131+4 
dilated rate = 4  conv3_2  211+4 
residual block 4  conv4_1  291+8 
dilated rate = 8  conv4_2  451+8 
residual block 5  conv5_1  611+16 
dilated rate = 16  conv5_2  931+16 
residual block 6  conv6_1  1251+32 
dilated rate = 32  conv6_2  1891+32 
residual block 7  conv7_1  2531+32 
dilated rate = 32  conv7_2  3171+32 
residual block 8  conv8_1  3811+32 
dilated rate = 32  conv8_2  4451+32 
model  parameters  Dice Coefficient  Pixel Accuracy  Recall  Precision  F1 score 
UNet  ~33M  0.918  0.943  0.839  0.987  0.907 
FusionNet  ~78M  0.944  0.969  0.877  0.997  0.933 
PDRUNet  ~0.36M  0.973  0.987  0.953  0.976  0.964 
model  parameters  Dice Coefficient  Pixel Accuracy  Recall  Precision  F1 score 
UNet  ~33M  0.918  0.943  0.839  0.987  0.907 
FusionNet  ~78M  0.944  0.969  0.877  0.997  0.933 
PDRUNet  ~0.36M  0.973  0.987  0.953  0.976  0.964 
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