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...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.展开更多
文摘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.
