Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets
Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets
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DOI：
10.1007/s10278-019-00308-x
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发表时间：
2020-01-16
影响因子：
4.4
通讯作者：
Bizzo, Bernardo C.
中科院分区：
文献类型：
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作者：
Gauriau, Romane;Bridge, Christopher;Bizzo, Bernardo C.
医疗保健领域对机器学习(ML)的兴趣与日俱增，这是由改善患者护理的承诺推动的。然而，目前有多少ML算法被用于临床实践？虽然这项技术已经存在，但由于缺乏基础设施、流程和工具，临床集成受到了阻碍，这一点在各种商业产品中都有所体现。特别是，自动选择特定算法的相关序列仍然具有挑战性。在这项工作中，我们提出了一种方法来自动识别大脑MRI序列，以便我们可以自动为进一步的图像相关算法选择相关输入。该方法依赖于医学数字成像和通信(DICOM)标准所要求的元数据，因此具有通用性和高效率(小于0.4ms/系列)。为了支持我们的主张，我们在来自两个不同大陆的两个不同机构的两个大型脑MRI数据集(总共40,000项研究)上测试了我们的方法。我们展示了高水平的准确性(从97.4%到99.96%)和机构的通用性。鉴于脑核磁共振成像方案的复杂性和多变性，我们相信类似的技术也可以应用于其他形式的放射成像。
The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.