Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

1. Murugesan, B, Sarveswaran, K, Shankaranarayana, SM, Ram, K, Joseph, J, Sivaprakasam, M. Psi-Net: shape and boundary aware joint multi-task deep network for medical image segmentation. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc. 2019;2019:7223-6.
Google Scholar2. Borgli, H, Thambawita, V, Smedsrud, PH, Hicks, S, Jha, D, Eskeland, SL, et al. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data. 2020;7(1):283.
Google Scholar | Crossref | Medline3. Qin, X, Zhang, Z, Huang, C, Gao, C, Dehghan, MJM. Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, June 15-20, 2019, pp. 7479-89.
Google Scholar4. Ronneberger, O, Fischer, P, Brox, T. U-Net: convolutional networks for biomedical image segmentation. In: Navab, N, Hornegger, J, Wells, W, Frangi, A eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Vol. 9351. Cham: Springer; 2015, pp. 234-41.
Google Scholar | Crossref5. Silva, J, Histace, A, Romain, O, Dray, X, Granado, B. Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg. 2014;9(2):283-93.
Google Scholar | Crossref | Medline6. Tajbakhsh, N, Gurudu, SR, Liang, J. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging. 2016;35(2):630-44.
Google Scholar | Crossref7. Arabi, H, Zaidi, H. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. Eur J Hybrid Imaging. 2020;4(1):17.
Google Scholar | Crossref | Medline8. Arabi, H, AkhavanAllaf, A, Sanaat, A, Shiri, I, Zaidi, H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med. 2021;83:122-37.
Google Scholar | Crossref | Medline9. Mohammadi, R, Shokatian, I, Salehi, M, Arabi, H, Shiri, I, Zaidi, H. Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer. Radiother Oncol. 2021;159:231-40.
Google Scholar | Crossref | Medline10. Shiri, I, Arabi, H, Salimi, Y, Sanaat, A, Akhavanallaf, A, Hajianfar, G, et al. COLI-Net: deep learning-assisted fully automated COVID -19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. Int J Imaging Syst Technol. Epub ahead of print 28 October 2021. doi: 10.1002/ima.22672.
Google Scholar11. Rajinikanth, V, Dey, N, Kumar, R, Panneerselvam, J, Raja, NSM. Fetal head periphery extraction from ultrasound image using Jaya algorithm and Chan-Vese segmentation. Procedia Comput Sci. 2019;152:66-73.
Google Scholar | Crossref12. Sobhaninia, Z, Rafiei, S, Emami, A, Karimi, N, Najarian, K, Samavi, S, et al. Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6545-8.
Google Scholar | Medline13. Shiri, I, Arabi, H, Sanaat, A, Jenabi, E, Becker, M, Zaidi, H. Fully automated gross tumor volume delineation from PET in head and neck cancer using deep learning algorithms. Clin Nucl Med. 2021;46:872-83.
Google Scholar | Crossref | Medline14. Rueda, S, Fathima, S, Knight, CL, Yaqub, M, Papageorghiou, AT, Rahmatullah, B, et al. Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging. 2013;33(4):797-813.
Google Scholar | Crossref | Medline15. Perez-Gonzalez, JL, Muńoz, JCB, Porras, MCR, Arámbula-Cosío, F, Medina-Bańuelos, V. Automatic fetal head measurements from ultrasound images using optimal ellipse detection and texture maps. IFMBE Proc. 2015;49:329-32.
Google Scholar | Crossref16. Zhang, J, Petitjean, C, Lopez, P, Ainouz, S. Direct estimation of fetal head circumference from ultrasound images based on regression CNN. Med Imaging Deep Learn. 2020;121:914-22.
Google Scholar17. Norman, B, Pedoia, V, Majumdar, S. Use of 2D U-net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology. 2018;288(1):177-85.
Google Scholar | Crossref | Medline18. Sevastopolsky, A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-net convolutional neural network. Pattern Recognit Image Anal. 2017;27(3):618-24.
Google Scholar | Crossref19. Roy, AG, Conjeti, S, Karri, SPK, Sheet, D, Katouzian, A, Wachinger, C, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express. 2017;8(8):3627-42.
Google Scholar | Crossref | Medline20. Alom, MZ, Hasan, M, Yakopcic, C, Taha, TM, Asari, VK. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv Preprint arXiv180206955; 2018.
Google Scholar21. Oktay, O, Schlemper, J, Folgoc, L., Le, Lee, M, Heinrich, M, Misawa, K, et al. Attention u-net: Learning where to look for the pancreas. arXiv Preprint arXiv180403999; 2018.
Google Scholar22. Zhou, SK, Greenspan, H, Shen, D. Deep Learning for Medical Image Analysis. Cambridge, MA: Academic Press; 2017.
Google Scholar23. Shen, D, Wu, G, Suk, H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48.
Google Scholar | Crossref | Medline24. Litjens, G, Kooi, T, Bejnordi, BE, Setio, AAA, Ciompi, F, Ghafoorian, M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
Google Scholar | Crossref | Medline25. Ait Skourt, B, El Hassani, A, Majda, A. Lung CT image segmentation using deep neural networks. Procedia Comput Sci. 2018;127:109-13.
Google Scholar | Crossref26. Yu, F, Koltun, V. Multi-scale context aggregation by dilated convolutions. arXiv Preprint arXiv151107122; 2016.
Google Scholar27. Jha, D, Riegler, MA, Johansen, D, Halvorsen, P, Johansen, HD. DoubleU-Net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, , pp. 558-64.
Google Scholar28. Moradi, S, Oghli, MG, Alizadehasl, A, Shiri, I, Oveisi, N, Oveisi, M, et al. MFP-Unet: a novel deep learning based approach for left ventricle segmentation in echocardiography. Phys Med. 2019;67:58-69.
Google Scholar | Crossref | Medline29. Gu, Z, Cheng, J, Fu, H, Zhou, K, Hao, H, Zhao, Y, et al. CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging. 2019;38(10):2281-92.
Google Scholar | Crossref30. Sun, J, Darbehani, F, Zaidi, M, Wang, B. SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020, pp. 797-806. Cham: Springer. Available from: https://link.springer.com/chapter/10.1007/978-3-030-59719-1_77 (accessed October 27, 2021).
Google Scholar31. Qiao, D, Zulkernine, F. Dilated squeeze-and-excitation U-Net for fetal ultrasound image segmentation. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020, Viña del Mar, Chile, 2020, pp. 1-7. Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc.
Google Scholar | Crossref32. Yang, Y, Yang, P, Zhang, B. Automatic segmentation in fetal ultrasound images based on improved U-net. J Phys Conf Ser. 2020;1693(1):012183.
Google Scholar | Crossref33. Ioffe, S, Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv Preprint arXiv150203167; 2015.
Google Scholar34. Dahl, GE, Sainath, TN, Hinton, GE. Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, , pp. 8609-13.
Google Scholar35. Chenarlogh, VA, Razzazi, F. Multi-stream 3D CNN structure for human action recognition trained by limited data. IET Comput Vis. 2019;13(3):338-44.
Google Scholar | Crossref36. van den Heuvel, TLA, de Bruijn, D, de Korte, CL, van Ginneken, B. Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One. 2018;13(8):e0200412.
Google Scholar | Crossref | Medline37. Bland, JM, Altman, DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47(8):931-6.
Google Scholar | Crossref38. Oghli, MG, Shabanzadeh, AAND, Moradi, S, Sirjani, N, Gerami, R, Ghaderi, P, et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med. 2021;88:127-37.
Google Scholar39. Ciurte, A, Bresson, X, Cuadra, MB. A semi-supervised patch-based approach for segmentation of fetal ultrasound imaging. In: Challenge US: Biometric Measurements From Fetal Ultrasound Images, ISBI 2012. 2012, pp. 5-7.
Google Scholar40. Stebbing, RV, McManigle, JE. A boundary fragment model for head segmentation in fetal ultrasound. In: Challenge US: Biometric Measurements From Fetal Ultrasound Images, ISBI 2012. 2012, pp. 9-11.
Google Scholar41. Sun, C. Automatic fetal head measurements from ultrasound images using circular shortest paths. In: Challenge US: Biometric Measurements From Fetal Ultrasound Images, ISBI 2012. 2012, pp. 13-5.
Google Scholar42. Ponomarev, GV, Gelfand, MS, Kazanov, MD. A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. In: Challenge US: Biometric Measurements From Fetal Ultrasound Images, ISBI 2012. 2012, pp. 17-9.
Google Scholar43. Rong, Y, Xiang, D, Zhu, W, Shi, F, Gao, E, Fan, Z, et al. Deriving external forces via convolutional neural networks for biomedical image segmentation. Biomed Opt Express. 2019;10(8):3800-14.
Google Scholar | Crossref44. Al-Bander, B, Alzahrani, T, Alzahrani, S, Williams, BM, Zheng, Y. Improving fetal head contour detection by object localisation with deep learning. In: Annual Conference on Medical Image Understanding and Analysis, 2019, pp. 142-50. Cham: Springer. Available from: https://link.springer.com/chapter/10.1007/978-3-030-39343-4_12 (accessed October 27, 2021).
Google Scholar45. Fiorentino, MC, Moccia, S, Capparuccini, M, Giamberini, S, Frontoni, E. A regression framework to head-circumference delineation from US fetal images. Comput Methods Programs Biomed. 2021;198:105771.
Google Scholar | Crossref | Medline

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