Ourselin Sébastien, Emberton Mark, Vercauteren Tom
Translational Imaging Group, Centre for Medical Image Computing, Dept. of Medical Physics & Biomedical Engineering, University College London, London, UK.
Division of Surgery & Interventional Science, University College London, London, UK; University College Hospitals NHS Foundation Trust, London, UK.
Med Image Anal. 2016 Oct;33:72-78. doi: 10.1016/j.media.2016.06.018. Epub 2016 Jun 15.
The early days of the field of medical image computing (MIC) and computer-assisted intervention (CAI), when publishing a strong self-contained methodological algorithm was enough to produce impact, are over. As a community, we now have substantial responsibility to translate our scientific progresses into improved patient care. In the field of computer-assisted interventions, the emphasis is also shifting from the mere use of well-known established imaging modalities and position trackers to the design and combination of innovative sensing, elaborate computational models and fine-grained clinical workflow analysis to create devices with unprecedented capabilities. The barriers to translating such devices in the complex and understandably heavily regulated surgical and interventional environment can seem daunting. Whether we leave the translation task mostly to our industrial partners or welcome, as researchers, an important share of it is up to us. We argue that embracing the complexity of surgical and interventional sciences is mandatory to the evolution of the field. Being able to do so requires large-scale infrastructure and a critical mass of expertise that very few research centres have. In this paper, we emphasise the need for a holistic approach to computer-assisted interventions where clinical, scientific, engineering and regulatory expertise are combined as a means of moving towards clinical impact. To ensure that the breadth of infrastructure and expertise required for translational computer-assisted intervention research does not lead to a situation where the field advances only thanks to a handful of exceptionally large research centres, we also advocate that solutions need to be designed to lower the barriers to entry. Inspired by fields such as particle physics and astronomy, we claim that centralised very large innovation centres with state of the art technology and health technology assessment capabilities backed by core support staff and open interoperability standards need to be accessible to the wider computer-assisted intervention research community.
医学图像计算（MIC）和计算机辅助干预（CAI）领域早期，发表一个强大的自包含方法算法就足以产生影响力的日子已经过去了。作为一个群体，我们现在肩负着重大责任，要将科学进展转化为改善患者护理。在计算机辅助干预领域，重点也在从仅仅使用知名的既定成像模式和位置跟踪器，转向创新传感、精细计算模型和细粒度临床工作流程分析的设计与组合，以制造具有前所未有的能力的设备。在复杂且可以理解受到严格监管的手术和介入环境中转化此类设备的障碍似乎令人生畏。是将翻译任务主要留给我们的行业合作伙伴，还是作为研究人员欣然承担其中重要份额，这取决于我们。我们认为，接受手术和介入科学的复杂性对该领域的发展至关重要。能够做到这一点需要大规模基础设施和大量专业知识，很少有研究中心具备这些条件。在本文中，我们强调需要一种整体方法来进行计算机辅助干预，将临床、科学、工程和监管专业知识结合起来，作为迈向临床影响的一种手段。为确保转化性计算机辅助干预研究所需的基础设施和专业知识的广度不会导致该领域仅靠少数几个特别大型的研究中心推动发展的局面，我们还主张需要设计解决方案来降低进入壁垒。受粒子物理学和天文学等领域的启发，我们声称，拥有先进技术和健康技术评估能力、有核心支持人员和开放互操作性标准支持的集中式超大型创新中心，应该可供更广泛的计算机辅助干预研究群体使用。
