医学成像中的仿真与综合。
Simulation and Synthesis in Medical Imaging.
出版信息
IEEE Trans Med Imaging. 2018 Mar;37(3):673-679. doi: 10.1109/TMI.2018.2800298.
Abstract
This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published in this issue covering both mechanistic (5) and phenomenological (7) medical image generation. This rich selection of papers covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology. It also covers all mainstream diagnostic medical imaging modalities. We conclude the editorial with a personal view on the field and highlight some existing challenges and future research opportunities.
摘要
本社论介绍了医学成像中的模拟和合成特刊。在本社论中，我们定义了医学成像中迄今为止模糊的模拟和合成术语。我们还简要讨论了基于机制（假设驱动）和基于现象（数据驱动）的医学图像生成模型的协同重要性。最后，我们介绍了本期发表的十二篇论文，涵盖了机制（5）和现象（7）医学图像生成。这组丰富的论文涵盖了心脏病学、视网膜病、组织病理学、神经科学和肿瘤学的应用。它还涵盖了所有主流的诊断医学成像模式。我们在社论的最后对该领域发表了个人看法，并强调了一些现有挑战和未来的研究机会。
相似文献
1
Simulation and Synthesis in Medical Imaging.医学成像中的仿真与综合。
IEEE Trans Med Imaging. 2018 Mar;37(3):673-679. doi: 10.1109/TMI.2018.2800298.
2
Image Reconstruction is a New Frontier of Machine Learning.图像重建是机器学习的一个新领域。
IEEE Trans Med Imaging. 2018 Jun;37(6):1289-1296. doi: 10.1109/TMI.2018.2833635.
3
Overview of deep learning in medical imaging.医学成像中的深度学习概述。
Radiol Phys Technol. 2017 Sep;10(3):257-273. doi: 10.1007/s12194-017-0406-5. Epub 2017 Jul 8.
4
Machine Learning Applications in Medical Image Analysis.机器学习在医学图像分析中的应用。
Comput Math Methods Med. 2017;2017:2361061. doi: 10.1155/2017/2361061. Epub 2017 Apr 13.
5
Sensor, Signal, and Imaging Informatics in 2017.2017年的传感器、信号与成像信息学
Yearb Med Inform. 2018 Aug;27(1):110-113. doi: 10.1055/s-0038-1667084. Epub 2018 Aug 29.
6
Mesh processing in medical-image analysis--a tutorial.医学图像分析中的网格处理——教程
IEEE Comput Graph Appl. 2012 Sep-Oct;32(5):22-8. doi: 10.1109/MCG.2012.91.
7
A survey on deep learning in medical image analysis.深度学习在医学图像分析中的应用研究综述。
Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26.
8
[Machine Learning for Computer-aided Diagnosis].[用于计算机辅助诊断的机器学习]
Igaku Butsuri. 2016;36(1):29-34. doi: 10.11323/jjmp.36.1_29.
9
Sensor, signal, and image informatics - state of the art and current topics.传感器、信号与图像信息学——现状与当前主题
Yearb Med Inform. 2006:57-67.
引用本文的文献
1
Generating and evaluating synthetic data in digital pathology through diffusion models.通过扩散模型在数字病理学中生成和评估合成数据。
Sci Rep. 2024 Nov 18;14(1):28435. doi: 10.1038/s41598-024-79602-w.
2
Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.利用从计算机断层扫描创建的合成数据提高 X 射线骨骼分割的对抗鲁棒性。
Sci Rep. 2024 Oct 28;14(1):25813. doi: 10.1038/s41598-024-73363-2.
3
Microwave Digital Twin Prototype for Shoulder Injury Detection.用于肩伤检测的微波数字孪生原型。
Sensors (Basel). 2024 Oct 16;24(20):6663. doi: 10.3390/s24206663.
4
Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection.基于超材料的微波成像传感器的设计与实验验证，用于乳腺癌、肺癌和脑癌的检测。
Sci Rep. 2024 Jul 13;14(1):16177. doi: 10.1038/s41598-024-67103-9.
5
Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.在T2加权液体衰减反转恢复磁共振图像中模拟采集偏移以对人工智能分割网络进行压力测试。
J Med Imaging (Bellingham). 2024 Mar;11(2):024013. doi: 10.1117/1.JMI.11.2.024013. Epub 2024 Apr 24.
6
Machine learning-based amide proton transfer imaging using partially synthetic training data.基于机器学习的酰胺质子转移成像技术，使用部分合成的训练数据。
Magn Reson Med. 2024 May;91(5):1908-1922. doi: 10.1002/mrm.29970. Epub 2023 Dec 14.
7
Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data.使用部分合成数据的机器学习方法对肿瘤进行酰胺质子转移（APT）成像。
ArXiv. 2023 Dec 13:arXiv:2311.01683v2.
8
An overview of synthetic administrative data for research.合成行政数据研究概述。
Int J Popul Data Sci. 2022 May 23;7(1):1727. doi: 10.23889/ijpds.v7i1.1727. eCollection 2022.
9
Assessing the transportability of clinical prediction models for cognitive impairment using causal models.使用因果模型评估认知障碍临床预测模型的可转移性。
BMC Med Res Methodol. 2023 Aug 19;23(1):187. doi: 10.1186/s12874-023-02003-6.
10
TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency.TISS-net：使用级联双任务网络和误差预测一致性进行脑肿瘤图像合成与分割
Neurocomputing (Amst). 2023 Aug 1;544:None. doi: 10.1016/j.neucom.2023.126295.
本文引用的文献
1
Artificial intelligence and deep learning - Radiology's next frontier?人工智能与深度学习——放射学的下一个前沿领域？
Clin Imaging. 2018 May-Jun;49:87-88. doi: 10.1016/j.clinimag.2017.11.007. Epub 2017 Nov 16.
2
When Machines Think: Radiology's Next Frontier.当机器开始思考：放射科的下一个前沿领域。
Radiology. 2017 Dec;285(3):713-718. doi: 10.1148/radiol.2017171183.
3
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.英国生物库前 10000 个脑成像数据集的图像处理和质量控制。
Neuroimage. 2018 Feb 1;166:400-424. doi: 10.1016/j.neuroimage.2017.10.034. Epub 2017 Oct 24.
4
A survey on deep learning in medical image analysis.深度学习在医学图像分析中的应用研究综述。
Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26.
5
Overview of deep learning in medical imaging.医学成像中的深度学习概述。
Radiol Phys Technol. 2017 Sep;10(3):257-273. doi: 10.1007/s12194-017-0406-5. Epub 2017 Jul 8.
6
Implementing Machine Learning in Radiology Practice and Research.在放射学实践与研究中实施机器学习
AJR Am J Roentgenol. 2017 Apr;208(4):754-760. doi: 10.2214/AJR.16.17224. Epub 2017 Jan 26.
7
Deep Learning for Health Informatics.用于健康信息学的深度学习
IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. doi: 10.1109/JBHI.2016.2636665. Epub 2016 Dec 29.
8
A framework to establish credibility of computational models in biology.建立生物学计算模型可信度的框架。
Prog Biophys Mol Biol. 2017 Oct;129:13-19. doi: 10.1016/j.pbiomolbio.2016.08.007. Epub 2016 Oct 1.
9
The Virtual Physiological Human: Ten Years After.虚拟生理人：十年之后
Annu Rev Biomed Eng. 2016 Jul 11;18:103-23. doi: 10.1146/annurev-bioeng-110915-114742.
10
Precision Imaging: more descriptive, predictive and integrative imaging.精准成像：更具描述性、预测性和整合性的成像技术。
Med Image Anal. 2016 Oct;33:27-32. doi: 10.1016/j.media.2016.06.024. Epub 2016 Jun 23.
Zotero 插件