Center for Medical Image Computing, University College London, London, WC1 2BT, UK.
Ann Biomed Eng. 2012 Feb;40(2):332-45. doi: 10.1007/s10439-011-0441-z. Epub 2011 Oct 20.
The emergence of Minimal Access Surgery (MAS) as a paradigm in modern healthcare treatment has created new challenges and opportunities for automated image understanding and computer vision. In MAS, images recovered from inside the body using specialized devices are used to visualize and operate on the surgical site but they can also be used to computationally infer in vivo 3D tissue surface shape, soft-tissue morphology, and surgical instrument motion. This information is important for facilitating in vivo biophotonic imaging modalities where the interaction between light and tissue is used to infer the structural and functional properties of the tissue. This article provides a review of the literature for computer vision and image understanding techniques applied to MAS images. The focus of this article is to elucidate a perspective on how computer vision techniques can be used to support and enhance the capabilities of biophotonic imaging modalities during surgery. Note that while MAS encompasses a variety of surgical specializations this review does not involve procedures performed in the interventional suite. The review has been carried out based on searches in the PubMed and IEEE databases using the article's keywords.
微创外科 (MAS) 的出现是现代医疗保健治疗的一个范例，为自动化图像理解和计算机视觉创造了新的挑战和机遇。在 MAS 中，使用专门设备从体内恢复的图像用于可视化和操作手术部位，但也可用于计算推断体内 3D 组织表面形状、软组织形态和手术器械运动。这些信息对于促进体内生物光子成像模式很重要，其中光与组织之间的相互作用用于推断组织的结构和功能特性。本文对应用于 MAS 图像的计算机视觉和图像理解技术进行了文献回顾。本文的重点是阐明如何使用计算机视觉技术来支持和增强手术过程中生物光子成像模式的能力。请注意，尽管 MAS 涵盖了各种外科专业，但本综述不涉及介入套件中进行的程序。该综述是根据在 PubMed 和 IEEE 数据库中使用文章关键词进行的搜索进行的。
