Reinertsen Ingerid, Collins D Louis, Drouin Simon
Department of Health Research, SINTEF Digital, Trondheim, Norway.
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Front Oncol. 2021 Feb 2;10:619274. doi: 10.3389/fonc.2020.619274. eCollection 2020.
With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets "Brain Images of Tumors for Evaluation" (BITE) and "Retrospective evaluation of Cerebral Tumors" (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.
随着机器学习和现代图形处理单元（GPU）的最新发展，术中超声（iUS）图像在手术过程中的处理和呈现方式发生了显著转变。对图像进行实时处理以突出重要解剖结构并结合原位显示，在指导手术时，有可能极大地促进iUS图像的采集和解读。为了充分利用机器学习的最新进展，开发和验证算法需要大量高质量的带注释训练数据。为确保高效收集足够数量的患者图像以及模型的外部有效性，训练数据应由不同的神经外科医生在多个中心收集，并以与最常用的机器学习工具包和库直接兼容的标准格式存储。在本文中，我们认为这种收集和组织大规模多中心数据集的工作应基于通用的开源软件和数据库。我们首先描述现有基于开源超声的神经导航系统的发展情况，以及这些系统在过去15年中如何为增强神经外科手术指导做出贡献。我们回顾了全球众多项目从公开可用的数据集“用于评估的脑肿瘤图像”（BITE）和“脑肿瘤回顾性评估”（RESECT）中受益的情况，这些数据集包含脑肿瘤病例的磁共振成像（MR）和超声数据。我们还描述了持续数据收集的必要性，以及如何通过使用一个适应性良好且用户友好的开源软件平台来组织这项工作，该平台集成了不断改进的指导和自动数据收集功能。
