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医学影像中关键点检测技术的应用与前景分析
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Abstract:
随着深度学习技术的迅速发展，关键点检测技术在医学影像分析中的应用受到广泛关注，尤其在超声、CT和MRI等医学影像中表现出巨大的潜力。文章首先回顾了传统的关键点检测技术与基于深度学习的关键点检测技术在医学影像中的应用，重点分析了卷积神经网络(CNN)、Hourglass网络和Transformer模型的特点与优势；随后讨论了关键点检测在医学影像中的实际应用，包括人体姿势估计、器官与肿瘤的分割与定位等领域的应用。此外，文章还总结了当前技术面临的挑战，如数据不足、图像噪声、跨设备泛化等问题，并提出了可能的解决方案。最后，结合深度学习的最新进展，本文展望了医学影像中关键点检测技术的未来发展趋势，旨在为医学影像分析中的关键点检测技术的研究与应用提供理论支持和发展思路。
With the rapid development of deep learning technology, the application of keypoint detection technology in medical image analysis has received widespread attention, especially in medical images such as ultrasound, CT, and MRI, showing great potential. The article first reviews the application of traditional keypoint detection techniques and deep learning based keypoint detection techniques in medical imaging, with a focus on analyzing the characteristics and advantages of convolutional neural networks (CNN), Hourglass networks, and Transformer models; Subsequently, the practical applications of keypoint detection in medical imaging were discussed, including human pose estimation, segmentation and localization of organs and tumors, and other fields. In addition, the article also summarizes the challenges currently faced by technology, such as severe data shortages, image noise, cross device generalization, and proposes possible solutions. Finally, based on the latest advances in deep learning, this article looks forward to the future development trends of keypoint detection technology in medical imaging, aiming to provide theoretical support and development ideas for the research and application of keypoint detection technology in medical image analysis.
| [1] | 俞瑞雪. 医学影像技术在医学影像诊断中的临床应用分析[J]. 国际全科医学, 2023, 4(3): 97-99. |
| [2] | Shu, C., He, Y. and Sun, Q. (2017) Point Cloud Registration Based on Convolutional Neural Network. Laser & Optoelectronics Progress, 54, Article ID: 031001. https://doi.org/10.3788/lop54.031001 |
| [3] | 罗会兰, 陈鸿坤. 基于深度学习的目标检测研究综述[J]. 电子学报, 2020, 48(6): 1230. |
| [4] | 高涵, 张明路, 张小俊, 等. 机械臂绝对定位精度标定关键技术综述[J]. 计算机应用研究, 2017, 34(9): 2570-2576. |
| [5] | 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251. |
| [6] | 余东行, 郭海涛, 张保明, 等. 级联卷积神经网络的遥感影像飞机目标检测[J]. 测绘学报, 2019, 48(8): 1046-1058. |
| [7] | 孙冠雄. 人体关键点检测模型及其向心脏关键点定位的迁移研究[D]: [硕士学位论文]. 哈尔滨: 哈尔滨工业大学, 2018. |
| [8] | 李梦荷, 许宏吉, 石磊鑫, 等. 基于骨骼关键点检测的多人行为识别[J]. 计算机科学, 2021, 48(4): 138-143. |
| [9] | 杨恒, 顾晨亮, 胡厚民, 等. 嵌入卷积增强型Transformer的头影解剖关键点检测[J]. 中国图象图形学报, 2023, 28(11): 3590-3601. |
| [10] | 韩冬, 李其花, 蔡巍, 等. 人工智能在医学影像中的研究与应用[J]. 大数据, 2019, 5(1): 39-67. |
| [11] | 章为川, 孔祥楠, 宋文. 图像的角点检测研究综述[J]. 电子学报, 2015, 43(11): 2315-2321. |
| [12] | 陆兴娟, 吴震宇. 图像边缘检测算法研究[J]. 现代电子技术, 2010, 33(6): 128-130. |
| [13] | 李健, 刘孔宇, 任宪盛, 等. 基于自适应阈值的Canny算法在MRI边缘检测中的应用[J]. 吉林大学学报(工学版), 2021, 51(2): 712-719. |
| [14] | 张小洪, 李博, 杨丹. 一种新的Harris多尺度角点检测[J]. 电子与信息学报, 2007, 29(7): 1735-1738. |
| [15] | 田娟, 郑郁正. 模板匹配技术在图像识别中的应用[J]. 传感器与微系统, 2008, 27(1): 112-114. |
| [16] | 张志强, 施文华. 改进的尺度不变特征变换算法并行加速双目测距系统及其实现[J]. 激光与光电子学进展, 2019, 56(14): 201-210. |
| [17] | Chandra, M.A. and Bedi, S.S. (2018) Survey on SVM and Their Application in Image Classification. International Journal of Information Technology, 13, 1-11. https://doi.org/10.1007/s41870-017-0080-1 |
| [18] | Hu, J. and Szymczak, S. (2023) A Review on Longitudinal Data Analysis with Random Forest. Briefings in Bioinformatics, 24, bbad002. https://doi.org/10.1093/bib/bbad002 |
| [19] | Sutskever, I. (2014) Advances in Neural Information Processing Systems. |
| [20] | Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90. |
| [21] | Gao, L., Chen, P. and Yu, S. (2016) Demonstration of Convolution Kernel Operation on Resistive Cross-Point Array. IEEE Electron Device Letters, 37, 870-873. https://doi.org/10.1109/led.2016.2573140 |
| [22] | Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S. and Miao, Y. (2021) Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sensing, 13, Article No. 4712. https://doi.org/10.3390/rs13224712 |
| [23] | Nassif, A.B., Shahin, I., Attili, I., Azzeh, M. and Shaalan, K. (2019) Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access, 7, 19143-19165. https://doi.org/10.1109/access.2019.2896880 |
| [24] | Zheng, Y., Liu, D., Georgescu, B., Nguyen, H. and Comaniciu, D. (2015) 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data. MICCAI 2015, Munich, 5-9 October 2015, 565-572. https://doi.org/10.1007/978-3-319-24553-9_69 |
| [25] | Hesamian, M.H., Jia, W., He, X. and Kennedy, P. (2019) Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging, 32, 582-596. https://doi.org/10.1007/s10278-019-00227-x |
| [26] | Susanto, Y., Livingstone, A.G., Ng, B.C. and Cambria, E. (2020) The Hourglass Model Revisited. IEEE Intelligent Systems, 35, 96-102. https://doi.org/10.1109/mis.2020.2992799 |
| [27] | Luo, Z., Wang, Z., Huang, Y., Wang, L., Tan, T. and Zhou, E. (2021) Rethinking the Heatmap Regression for Bottom-Up Human Pose Estimation. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 19-25 June 2021, 13264-13273. https://doi.org/10.1109/cvpr46437.2021.01306 |
| [28] | Hervella, Á.S., Rouco, J., Novo, J., Penedo, M.G. and Ortega, M. (2020) Deep Multi-Instance Heatmap Regression for the Detection of Retinal Vessel Crossings and Bifurcations in Eye Fundus Images. Computer Methods and Programs in Biomedicine, 186, Article ID: 105201. https://doi.org/10.1016/j.cmpb.2019.105201 |
| [29] | Han, K., Xiao, A., Wu, E., et al. (2021) Transformer in Transformer. Proceedings of the 35th International Conference on Neural Information Processing Systems, 6-14 December 2021, 15908-15919. |
| [30] | Shamshad, F., Khan, S., Zamir, S.W., Khan, M.H., Hayat, M., Khan, F.S., et al. (2023) Transformers in Medical Imaging: A Survey. Medical Image Analysis, 88, Article ID: 102802. https://doi.org/10.1016/j.media.2023.102802 |
| [31] | Yang, S., Quan, Z., Nie, M. and Yang, W. (2021) Transpose: Keypoint Localization via Transformer. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 11-17 October 2021, 11802-11812. https://doi.org/10.1109/iccv48922.2021.01159 |
| [32] | Lin, A., Chen, B., Xu, J., Zhang, Z., Lu, G. and Zhang, D. (2022) DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation. IEEE Transactions on Instrumentation and Measurement, 71, 1-15. https://doi.org/10.1109/tim.2022.3178991 |
| [33] | Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., et al. (2023) A Survey on Vision Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 87-110. https://doi.org/10.1109/tpami.2022.3152247 |
| [34] | Kassem, M.A., Hosny, K.M. and Fouad, M.M. (2020) Skin Lesions Classification into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning. IEEE Access, 8, 114822-114832. https://doi.org/10.1109/access.2020.3003890 |
| [35] | Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al. (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34, 1993-2024. https://doi.org/10.1109/tmi.2014.2377694 |
| [36] | Naseer, I., Akram, S., Masood, T., Jaffar, A., Khan, M.A. and Mosavi, A. (2022) Performance Analysis of State-of-the-Art CNN Architectures for Luna16. Sensors, 22, Article No. 4426. https://doi.org/10.3390/s22124426 |
| [37] | Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B. and Bharath, A.A. (2018) Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine, 35, 53-65. https://doi.org/10.1109/msp.2017.2765202 |
| [38] | Pan, S.J. (2020) Transfer Learning. Learning, 21, 1-2. |
| [39] | Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D. and Makedon, F. (2020) A Survey on Contrastive Self-Supervised Learning. Technologies, 9, Article No. 2. https://doi.org/10.3390/technologies9010002 |
| [40] | Zhang, Y. and Yang, Q. (2017) An Overview of Multi-Task Learning. National Science Review, 5, 30-43. https://doi.org/10.1093/nsr/nwx105 |
| [41] | Sinha, D. and El-Sharkawy, M. (2019) Thin MobileNet: An Enhanced MobileNet Architecture. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, 10-12 October 2019, 280-285. https://doi.org/10.1109/uemcon47517.2019.8993089 |
| [42] | Koonce, B. (2021) EfficientNet. In: Koonce, B., Ed., Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Apress, 109-123. https://doi.org/10.1007/978-1-4842-6168-2_10 |
| [43] | Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D. and Zhu, J. (2019) Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In: Tang, J., et al., Eds., Natural Language Processing and Chinese Computing, Springer International Publishing, 563-574. https://doi.org/10.1007/978-3-030-32236-6_51 |
