Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

1. Wen, SW, Liu, S, Joseph, KS, Rouleau, J, Allen, A. Patterns of infant mortality caused by major congenital anomalies. Teratology. 2000;61(5):342-6.
Google Scholar | Crossref | Medline2. van der Linde, D, Konings, EE, Slager, MA, Witsenburg, M, Helbing, WA, Takkenberg, JJ, et al. Birth prevalence of congenital heart disease worldwide a systematic review and meta-analysis. J Am Coll Cardiol. 2011;58(21):2241-7.
Google Scholar | Crossref | Medline3. Yun, SW. Congenital heart disease in the newborn requiring early intervention. Korean J Pediatr. 2011;54(5):183-91.
Google Scholar | Crossref | Medline4. Smith, GC, Wood, AM, White, IR, Pell, JP, Cameron, AD, Dobbie, R. Neonatal respiratory morbidity at term and the risk of childhood asthma. Arch Dis Child. 2004;89(10):956-60.
Google Scholar | Crossref5. Wurzel, DF, Chang, AB. An update on pediatric bronchiectasis. Expert Rev Respir Med. 2017;11(7):517-32.
Google Scholar | Crossref | Medline6. Thébaud, B. Update in pediatric lung disease 2010. Am J Respir Crit Care Med. 2011;183(11):1477-81.
Google Scholar | Crossref | Medline7. Huang, Q, Chen, Y, Liu, L, Tao, D, Li, X. On combining biclustering mining and adaboost for breast tumor classification. IEEE Trans Knowl Data Eng. 2019;32(4):728-38.
Google Scholar | Crossref8. Clough, JR, Khanal, B, van Poppel, MP, Skelton, E, Matthews, J, Schnabel, JA. Image reconstruction in a manifold of image patches: application to whole-fetus ultrasound imaging. In: Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, , Shenzhen, China, vol 11905, p. 226. Springer Nature.
Google Scholar9. Sundaresan, V, Bridge, CP, Ioannou, C, Noble, JA. Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), , Melbourne, VIC, pp. 671–74. IEEE.
Google Scholar10. Liang, S, Yang, F, Wen, T, Yao, Z, Huang, Q, Ye, C. Nonlocal total variation based on symmetric kullback-leibler divergence for the ultrasound image despeckling. BMC Med Imaging. 2017;17(1):57.
Google Scholar | Crossref | Medline11. Yu, Z, Tan, EL, Ni, D, Qin, J, Chen, S, Li, S, et al. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J Biomed Health Inform. 2018;22(3):874-85.
Google Scholar | Crossref12. Huang, Q, Huang, Y, Luo, Y, Yuan, F, Li, X. Segmentation of breast ultrasound image with semantic classification of superpixels. Med Image Anal. 2020;61:101657.
Google Scholar | Crossref | Medline13. Jardim, SMG, Figueiredo, MAT. Segmentation of fetal ultrasound images. Ultrasound Med Biol. 2005;31(2):243-50.
Google Scholar | Crossref | Medline14. Xian, M, Zhang, Y, Cheng, HD. Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recognit. 2015;48(2):485-97.
Google Scholar | Crossref15. Moon, WK, Lo, CM, Chen, RT, Shen, YW, Chang, JM, Huang, CS, et al. Tumor detection in automated breast ultrasound images using quantitative tissue clustering. Med Phys. 2014;41(4):042901.
Google Scholar | Crossref16. Lo, CM, Chen, RT, Chang, YC, Yang, YW, Hung, MJ, Huang, CS, et al. Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. IEEE Trans Med Imaging. 2014;33(7):1503-11.
Google Scholar | Crossref17. Chang, H, Chen, Z, Huang, Q, Shi, J, Li, X. Graph-based learning for segmentation of 3d ultrasound images. Neurocomputing. 2015;151:632-44.
Google Scholar | Crossref18. Gao, L, Liu, X, Chen, W. Phase-and GVF-based level set segmentation of ultrasonic breast tumors. J Appl Math. 2012;2012:1–22.
Google Scholar | Crossref19. Xian, M, Huang, J, Zhang, Y, Tang, X. Multiple-domain knowledge based mrf model for tumor segmentation in breast ultrasound images. In: 2012 19th IEEE International Conference on Image Processing, , Orlando, FL, pp. 2021–24. IEEE.
Google Scholar20. Huang, Q, Luo, Y, Zhang, Q. Breast ultrasound image segmentation: a survey. Int J Comput Assist Radiol Surg. 2017;12(3):493-507.
Google Scholar | Crossref | Medline21. Luo, F, Wang, M, Liu, Y, Zhao, XM, Li, A. Deepphos: prediction of protein phosphorylation sites with deep learning. Bioinformatics. 2019;35(16):2766-73.
Google Scholar | Crossref22. Asgari Taghanaki, S, Abhishek, K, Cohen, JP, Cohen-Adad, J, Hamarneh, G. Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev. 2021;54:137-78.
Google Scholar | Crossref | Medline23. Yap, MH, Goyal, M, Osman, FM, Martí, R, Denton, E, Juette, A, et al. Breast ultrasound lesions recognition: end-to-end deep learning approaches. J Med Imaging. 2019;6(1):011007.
Google Scholar24. Ronneberger, O, Fischer, P, Brox, T. U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, , Munich, Germany, pp. 234–41. Springer.
Google Scholar25. Paszke, A, Chaurasia, A, Kim, S, Culurciello, E. Enet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147, 2016.
Google Scholar26. Jégou, S, Drozdzal, M, Vazquez, D, Romero, A, Bengio, Y. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, , Honolulu, HI, pp. 11–9.
Google Scholar27. Shi, J, Zhou, S, Liu, X, Zhang, Q, Lu, M, Wang, T. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing. 2016;194:87-94.
Google Scholar | Crossref28. Vásquez, P, Arana, N, Izaguirre, A, Burgos, J. Labor induction failure prediction based on b-mode ultrasound image processing using multiscale local binary patterns. In: 2016 International Conference on Optoelectronics and Image Processing (ICOIP), , Warsaw, Poland, pp. 25–9. IEEE.
Google Scholar29. Zhao, H, Shi, J, Qi, X, Wang, X, Jia, J. Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , Honolulu, HI, pp. 2881–90.
Google Scholar30. Athira, PK, Mathew, LS. Fetal anomaly detection in ultrasound image. Int J Comput Appl. 2015;975:8887.
Google Scholar31. Lin, M, Chen, Q, Yan, S. Network in network. arXiv preprint arXiv:1312.4400, 2013.
Google Scholar32. Gao, SH, Cheng, MM, Zhao, K, Zhang, XY, Yang, MH, Torr, P. Res2net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell. 2021;43(2):652-62.
Google Scholar | Crossref33. Oktay, O, Schlemper, J, Folgoc, LL, Lee, M, Heinrich, M, Misawa, K, et al. Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, 2018.
Google Scholar34. Buslaev, A, Iglovikov, VI, Khvedchenya, E, Parinov, A, Druzhinin, M, Kalinin, AA. Albumentations: fast and flexible image augmentations. Information. 2020;11(2):125.
Google Scholar | Crossref35. Krizhevsky, A, Sutskever, I, Hinton, GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097–105.
Google Scholar | Medline36. Chen, LC, Yang, Y, Wang, J, Xu, W, Yuille, AL. (2016). Attention to scale: scale-aware semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , Las Vegas, NV, pp. 3640–9.
Google Scholar37. Anderson, P, He, X, Buehler, C, Teney, D, Johnson, M, Gould, S, et al. Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , Salt Lake City, UT, pp. 6077–86.
Google Scholar38. Shen, T, Zhou, T, Long, G, Jiang, J, Pan, S, Zhang, C. Disan: directional self-attention network for RNN/CNN-free language understanding. arXiv preprint arXiv:1709.04696, 2017.
Google Scholar39. Zhou, D, Li, M, Li, Y, Qi, J, Liu, K, Cong, X, et al. Detection of ground straw coverage under conservation tillage based on deep learning. Comput Electron Agric. 2020;172:105369.
Google Scholar | Crossref

留言 (0)

沒有登入
gif