Center for Visual Computing, CentraleSupelec, INRIA, Universite Paris-Saclay, Grande Voie des Vignes, Chatenay-Malabry, 92295, France; Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London, SW7 2AZ, UK.
Center for Visual Computing, CentraleSupelec, INRIA, Universite Paris-Saclay, Grande Voie des Vignes, Chatenay-Malabry, 92295, France; TheraPanacea, 24 Rue du Faubourg Saint-Jacques, 75014, Paris, France.
Med Image Anal. 2017 Jul;39:101-123. doi: 10.1016/j.media.2017.04.010. Epub 2017 Apr 28.
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.
在过去的几十年中,医学成像研究领域见证了图像配准方法的不断进步,这些进步推动了技术的发展,使新的医学程序成为可能。一种特殊类型的图像配准问题,称为切片到体积配准,在图像引导手术和体积图像重建等领域发挥了重要作用。然而,到目前为止,尽管关于这个主题有广泛的文献,但还没有写一篇综述来讨论这个具有挑战性的问题。本文介绍了关于切片到体积配准文献的第一个全面调查,根据一个特定的分类法对算法进行了分类研究,并分析了每个类别优缺点。我们从这个分析中得出了一些一般性的结论,并对该领域的未来提出了我们的看法。