Maier-Hein Lena, Mersmann Sven, Kondermann Daniel, Bodenstedt Sebastian, Sanchez Alexandro, Stock Christian, Kenngott Hannes Gotz, Eisenmann Mathias, Speidel Stefanie
Med Image Comput Comput Assist Interv. 2014;17(Pt 2):438-45. doi: 10.1007/978-3-319-10470-6_55.
Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.
机器学习算法在计算机辅助干预领域越来越受到关注。然而，迄今为止的瓶颈之一是训练数据的可用性，这些数据通常由资源非常有限的医学专家生成。众包是一种新趋势，它基于将认知任务外包给在线社区中许多匿名的未经培训的个人。在这项工作中，我们研究了众包在内窥镜图像数据中分割医疗器械的潜力。我们的研究表明：（1）从多个匿名非专家的注释计算出的分割结果与医学专家的分割结果相当；（2）由众包生成的训练数据与医学专家注释的数据质量相同。鉴于注释速度、可扩展性和低成本，这意味着科学界可能不再需要依赖专家为某些应用生成参考或训练数据。为了引发对内窥镜图像处理的进一步研究，本研究中使用的数据将公开提供。
