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首页> 外文期刊>Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology >Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research
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Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research

机译：基于云的NoSQL肺结节开放数据库，用于计算机辅助肺癌诊断和可重复性研究

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摘要

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.
机译：肺癌是世界上与癌症相关的死亡的主要原因，其主要表现是肺结节。肺结节的检测和分类是具有挑战性的任务，必须由合格的专家来完成，但是图像解释错误使这些任务变得困难。为了帮助放射科医生完成这些艰巨的任务，将基于计算机的工具与病变检测，病理诊断和图像解释过程相集成非常重要。但是，计算机辅助诊断研究面临的问题是，没有足够的共享医学参考数据用于诊断计算方法的开发，测试和评估。为了最大程度地减少此问题，本文介绍了一个公共的基于非关系文档的基于云的基于云的数据库，该数据库具有3D纹理属性，由经验丰富的放射科医生识别并由同一位专家分类为9种不同的主观特征。我们开发此数据库的目标是，通过将该数据库部署在云数据库即服务框架中来改善计算机辅助的肺癌诊断以及肺结节的检测和分类研究。肺结节数据由肺图像数据库联盟和图像数据库资源倡议（LIDC-IDRI）提供，图像描述符通过体积纹理分析获得，并且数据库模式是使用面向文档的不仅结构化查询语言（NoSQL）开发的方法。提议的数据库现在包含379个检查，838个结节和8237个图像，其中4029个是CT扫描，还有4208个手动分段的结节，并且已在云基础架构上的MongoDB实例中分配。

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